Scheduled Commit

data/clustering_battle-3d6410bd-df84-4241-a31a-72548a10ef27.jsonl

@@ -1 +1,2 @@
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+{"tstamp": 1734943387.5305, "task_type": "clustering", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "606549ec1b68473ab1ebf4f888a5165f", "0_model_name": "text-embedding-004", "0_prompt": ["Shanghai", "Beijing", "Shenzhen", "Hangzhou", "Seattle", "Boston", "New York", "San Francisco", "octagon", "rectangle", "Temple of Artemis", "Colossus of Rhodes", "Statue of Zeus", "Lighthouse of Alexandria", "Hanging Gardens of Babylon", "Pyramids of Giza", "brunette", "black", "blonde", "redhead", "gray", "auburn", "white", "soccer", "basketball", "tennis", "baseball", "cricket", "ruby", "topaz", "diamond", "octagon", "rectangle", "Temple of Artemis", "Colossus of Rhodes", "Statue of Zeus", "Lighthouse of Alexandria", "Hanging Gardens of Babylon", "Pyramids of Giza", "brunette", "black", "blonde", "redhead", "gray", "auburn", "white", "soccer", "basketball", "tennis", "baseball", "cricket", "ruby", "topaz", "diamond", "octagon", "rectangle", "Temple of Artemis", "Colossus of Rhodes", "Statue of Zeus", "Lighthouse of Alexandria", "Hanging Gardens of Babylon", "Pyramids of Giza", "brunette", "black", "blonde", "redhead", "gray", "auburn", "white", "soccer", "basketball", "tennis", "baseball", "cricket", "ruby", "topaz", "diamond", "octagon", "rectangle", "Temple of Artemis", "Colossus of Rhodes", "Statue of Zeus", "Lighthouse of Alexandria", "Hanging Gardens of Babylon", "Pyramids of Giza", "brunette", "black", "blonde", "redhead", "gray", "auburn", "white", "soccer", "basketball", "tennis", "baseball", "cricket", "ruby", "topaz", "diamond"], "0_ncluster": 5, "0_output": "", "0_ndim": "3D (press for 2D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "9f402924b8d9412d9bcdb8e83a97118b", "1_model_name": "GritLM/GritLM-7B", "1_prompt": ["Shanghai", "Beijing", "Shenzhen", "Hangzhou", "Seattle", "Boston", "New York", "San Francisco", "octagon", "rectangle", "Temple of Artemis", "Colossus of Rhodes", "Statue of Zeus", "Lighthouse of Alexandria", "Hanging Gardens of Babylon", "Pyramids of Giza", "brunette", "black", "blonde", "redhead", "gray", "auburn", "white", "soccer", "basketball", "tennis", "baseball", "cricket", "ruby", "topaz", "diamond", "octagon", "rectangle", "Temple of Artemis", "Colossus of Rhodes", "Statue of Zeus", "Lighthouse of Alexandria", "Hanging Gardens of Babylon", "Pyramids of Giza", "brunette", "black", "blonde", "redhead", "gray", "auburn", "white", "soccer", "basketball", "tennis", "baseball", "cricket", "ruby", "topaz", "diamond", "octagon", "rectangle", "Temple of Artemis", "Colossus of Rhodes", "Statue of Zeus", "Lighthouse of Alexandria", "Hanging Gardens of Babylon", "Pyramids of Giza", "brunette", "black", "blonde", "redhead", "gray", "auburn", "white", "soccer", "basketball", "tennis", "baseball", "cricket", "ruby", "topaz", "diamond", "octagon", "rectangle", "Temple of Artemis", "Colossus of Rhodes", "Statue of Zeus", "Lighthouse of Alexandria", "Hanging Gardens of Babylon", "Pyramids of Giza", "brunette", "black", "blonde", "redhead", "gray", "auburn", "white", "soccer", "basketball", "tennis", "baseball", "cricket", "ruby", "topaz", "diamond"], "1_ncluster": 5, "1_output": "", "1_ndim": "3D (press for 2D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}

data/clustering_individual-3d6410bd-df84-4241-a31a-72548a10ef27.jsonl

@@ -2,3 +2,11 @@
 {"tstamp": 1734940899.0673, "task_type": "clustering", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1734940861.4652, "finish": 1734940899.0673, "ip": "", "conv_id": "01d1a51939c14e3e80e64b86215d95df", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": ["If someone online buys something off of my Amazon wish list, do they get my full name and address?", "Package \"In Transit\" over a week. No scheduled delivery date, no locations. What's up?", "Can Amazon gift cards replace a debit card?", "Homesick GWS star Cameron McCarthy on road to recovery", "Accidently ordered 2 of an item, how do I only return 1? For free?", "Need help ASAP, someone ordering in my account", "So who's everyone tipping for Round 1?"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1734943106.9982, "task_type": "clustering", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1734943106.7236, "finish": 1734943106.9982, "ip": "", "conv_id": "606549ec1b68473ab1ebf4f888a5165f", "model_name": "text-embedding-004", "prompt": ["Shanghai", "Beijing", "Shenzhen", "Hangzhou", "Seattle", "Boston", "New York", "San Francisco"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1734943106.9982, "task_type": "clustering", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1734943106.7236, "finish": 1734943106.9982, "ip": "", "conv_id": "9f402924b8d9412d9bcdb8e83a97118b", "model_name": "GritLM/GritLM-7B", "prompt": ["Shanghai", "Beijing", "Shenzhen", "Hangzhou", "Seattle", "Boston", "New York", "San Francisco"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1734943151.1173, "task_type": "clustering", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1734943150.8008, "finish": 1734943151.1173, "ip": "", "conv_id": "606549ec1b68473ab1ebf4f888a5165f", "model_name": "text-embedding-004", "prompt": ["Shanghai", "Beijing", "Shenzhen", "Hangzhou", "Seattle", "Boston", "New York", "San Francisco", "octagon", "rectangle", "Temple of Artemis", "Colossus of Rhodes", "Statue of Zeus", "Lighthouse of Alexandria", "Hanging Gardens of Babylon", "Pyramids of Giza", "brunette", "black", "blonde", "redhead", "gray", "auburn", "white", "soccer", "basketball", "tennis", "baseball", "cricket", "ruby", "topaz", "diamond"], "ncluster": 5, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1734943151.1173, "task_type": "clustering", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1734943150.8008, "finish": 1734943151.1173, "ip": "", "conv_id": "9f402924b8d9412d9bcdb8e83a97118b", "model_name": "GritLM/GritLM-7B", "prompt": ["Shanghai", "Beijing", "Shenzhen", "Hangzhou", "Seattle", "Boston", "New York", "San Francisco", "octagon", "rectangle", "Temple of Artemis", "Colossus of Rhodes", "Statue of Zeus", "Lighthouse of Alexandria", "Hanging Gardens of Babylon", "Pyramids of Giza", "brunette", "black", "blonde", "redhead", "gray", "auburn", "white", "soccer", "basketball", "tennis", "baseball", "cricket", "ruby", "topaz", "diamond"], "ncluster": 5, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1734943193.8288, "task_type": "clustering", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1734943182.3278, "finish": 1734943193.8288, "ip": "", "conv_id": "606549ec1b68473ab1ebf4f888a5165f", "model_name": "text-embedding-004", "prompt": ["Shanghai", "Beijing", "Shenzhen", "Hangzhou", "Seattle", "Boston", "New York", "San Francisco", "octagon", "rectangle", "Temple of Artemis", "Colossus of Rhodes", "Statue of Zeus", "Lighthouse of Alexandria", "Hanging Gardens of Babylon", "Pyramids of Giza", "brunette", "black", "blonde", "redhead", "gray", "auburn", "white", "soccer", "basketball", "tennis", "baseball", "cricket", "ruby", "topaz", "diamond", "octagon", "rectangle", "Temple of Artemis", "Colossus of Rhodes", "Statue of Zeus", "Lighthouse of Alexandria", "Hanging Gardens of Babylon", "Pyramids of Giza", "brunette", "black", "blonde", "redhead", "gray", "auburn", "white", "soccer", "basketball", "tennis", "baseball", "cricket", "ruby", "topaz", "diamond"], "ncluster": 5, "output": "", "ndim": "3D (press for 2D)", "dim_method": "UMAP", "clustering_method": "KMeans"}
+{"tstamp": 1734943193.8288, "task_type": "clustering", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1734943182.3278, "finish": 1734943193.8288, "ip": "", "conv_id": "9f402924b8d9412d9bcdb8e83a97118b", "model_name": "GritLM/GritLM-7B", "prompt": ["Shanghai", "Beijing", "Shenzhen", "Hangzhou", "Seattle", "Boston", "New York", "San Francisco", "octagon", "rectangle", "Temple of Artemis", "Colossus of Rhodes", "Statue of Zeus", "Lighthouse of Alexandria", "Hanging Gardens of Babylon", "Pyramids of Giza", "brunette", "black", "blonde", "redhead", "gray", "auburn", "white", "soccer", "basketball", "tennis", "baseball", "cricket", "ruby", "topaz", "diamond", "octagon", "rectangle", "Temple of Artemis", "Colossus of Rhodes", "Statue of Zeus", "Lighthouse of Alexandria", "Hanging Gardens of Babylon", "Pyramids of Giza", "brunette", "black", "blonde", "redhead", "gray", "auburn", "white", "soccer", "basketball", "tennis", "baseball", "cricket", "ruby", "topaz", "diamond"], "ncluster": 5, "output": "", "ndim": "3D (press for 2D)", "dim_method": "UMAP", "clustering_method": "KMeans"}
+{"tstamp": 1734943246.0173, "task_type": "clustering", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1734943244.2712, "finish": 1734943246.0173, "ip": "", "conv_id": "606549ec1b68473ab1ebf4f888a5165f", "model_name": "text-embedding-004", "prompt": ["Shanghai", "Beijing", "Shenzhen", "Hangzhou", "Seattle", "Boston", "New York", "San Francisco", "octagon", "rectangle", "Temple of Artemis", "Colossus of Rhodes", "Statue of Zeus", "Lighthouse of Alexandria", "Hanging Gardens of Babylon", "Pyramids of Giza", "brunette", "black", "blonde", "redhead", "gray", "auburn", "white", "soccer", "basketball", "tennis", "baseball", "cricket", "ruby", "topaz", "diamond", "octagon", "rectangle", "Temple of Artemis", "Colossus of Rhodes", "Statue of Zeus", "Lighthouse of Alexandria", "Hanging Gardens of Babylon", "Pyramids of Giza", "brunette", "black", "blonde", "redhead", "gray", "auburn", "white", "soccer", "basketball", "tennis", "baseball", "cricket", "ruby", "topaz", "diamond", "octagon", "rectangle", "Temple of Artemis", "Colossus of Rhodes", "Statue of Zeus", "Lighthouse of Alexandria", "Hanging Gardens of Babylon", "Pyramids of Giza", "brunette", "black", "blonde", "redhead", "gray", "auburn", "white", "soccer", "basketball", "tennis", "baseball", "cricket", "ruby", "topaz", "diamond"], "ncluster": 5, "output": "", "ndim": "3D (press for 2D)", "dim_method": "TSNE", "clustering_method": "KMeans"}
+{"tstamp": 1734943246.0173, "task_type": "clustering", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1734943244.2712, "finish": 1734943246.0173, "ip": "", "conv_id": "9f402924b8d9412d9bcdb8e83a97118b", "model_name": "GritLM/GritLM-7B", "prompt": ["Shanghai", "Beijing", "Shenzhen", "Hangzhou", "Seattle", "Boston", "New York", "San Francisco", "octagon", "rectangle", "Temple of Artemis", "Colossus of Rhodes", "Statue of Zeus", "Lighthouse of Alexandria", "Hanging Gardens of Babylon", "Pyramids of Giza", "brunette", "black", "blonde", "redhead", "gray", "auburn", "white", "soccer", "basketball", "tennis", "baseball", "cricket", "ruby", "topaz", "diamond", "octagon", "rectangle", "Temple of Artemis", "Colossus of Rhodes", "Statue of Zeus", "Lighthouse of Alexandria", "Hanging Gardens of Babylon", "Pyramids of Giza", "brunette", "black", "blonde", "redhead", "gray", "auburn", "white", "soccer", "basketball", "tennis", "baseball", "cricket", "ruby", "topaz", "diamond", "octagon", "rectangle", "Temple of Artemis", "Colossus of Rhodes", "Statue of Zeus", "Lighthouse of Alexandria", "Hanging Gardens of Babylon", "Pyramids of Giza", "brunette", "black", "blonde", "redhead", "gray", "auburn", "white", "soccer", "basketball", "tennis", "baseball", "cricket", "ruby", "topaz", "diamond"], "ncluster": 5, "output": "", "ndim": "3D (press for 2D)", "dim_method": "TSNE", "clustering_method": "KMeans"}
+{"tstamp": 1734943278.7661, "task_type": "clustering", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1734943278.1578, "finish": 1734943278.7661, "ip": "", "conv_id": "606549ec1b68473ab1ebf4f888a5165f", "model_name": "text-embedding-004", "prompt": ["Shanghai", "Beijing", "Shenzhen", "Hangzhou", "Seattle", "Boston", "New York", "San Francisco", "octagon", "rectangle", "Temple of Artemis", "Colossus of Rhodes", "Statue of Zeus", "Lighthouse of Alexandria", "Hanging Gardens of Babylon", "Pyramids of Giza", "brunette", "black", "blonde", "redhead", "gray", "auburn", "white", "soccer", "basketball", "tennis", "baseball", "cricket", "ruby", "topaz", "diamond", "octagon", "rectangle", "Temple of Artemis", "Colossus of Rhodes", "Statue of Zeus", "Lighthouse of Alexandria", "Hanging Gardens of Babylon", "Pyramids of Giza", "brunette", "black", "blonde", "redhead", "gray", "auburn", "white", "soccer", "basketball", "tennis", "baseball", "cricket", "ruby", "topaz", "diamond", "octagon", "rectangle", "Temple of Artemis", "Colossus of Rhodes", "Statue of Zeus", "Lighthouse of Alexandria", "Hanging Gardens of Babylon", "Pyramids of Giza", "brunette", "black", "blonde", "redhead", "gray", "auburn", "white", "soccer", "basketball", "tennis", "baseball", "cricket", "ruby", "topaz", "diamond", "octagon", "rectangle", "Temple of Artemis", "Colossus of Rhodes", "Statue of Zeus", "Lighthouse of Alexandria", "Hanging Gardens of Babylon", "Pyramids of Giza", "brunette", "black", "blonde", "redhead", "gray", "auburn", "white", "soccer", "basketball", "tennis", "baseball", "cricket", "ruby", "topaz", "diamond"], "ncluster": 5, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1734943278.7661, "task_type": "clustering", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1734943278.1578, "finish": 1734943278.7661, "ip": "", "conv_id": "9f402924b8d9412d9bcdb8e83a97118b", "model_name": "GritLM/GritLM-7B", "prompt": ["Shanghai", "Beijing", "Shenzhen", "Hangzhou", "Seattle", "Boston", "New York", "San Francisco", "octagon", "rectangle", "Temple of Artemis", "Colossus of Rhodes", "Statue of Zeus", "Lighthouse of Alexandria", "Hanging Gardens of Babylon", "Pyramids of Giza", "brunette", "black", "blonde", "redhead", "gray", "auburn", "white", "soccer", "basketball", "tennis", "baseball", "cricket", "ruby", "topaz", "diamond", "octagon", "rectangle", "Temple of Artemis", "Colossus of Rhodes", "Statue of Zeus", "Lighthouse of Alexandria", "Hanging Gardens of Babylon", "Pyramids of Giza", "brunette", "black", "blonde", "redhead", "gray", "auburn", "white", "soccer", "basketball", "tennis", "baseball", "cricket", "ruby", "topaz", "diamond", "octagon", "rectangle", "Temple of Artemis", "Colossus of Rhodes", "Statue of Zeus", "Lighthouse of Alexandria", "Hanging Gardens of Babylon", "Pyramids of Giza", "brunette", "black", "blonde", "redhead", "gray", "auburn", "white", "soccer", "basketball", "tennis", "baseball", "cricket", "ruby", "topaz", "diamond", "octagon", "rectangle", "Temple of Artemis", "Colossus of Rhodes", "Statue of Zeus", "Lighthouse of Alexandria", "Hanging Gardens of Babylon", "Pyramids of Giza", "brunette", "black", "blonde", "redhead", "gray", "auburn", "white", "soccer", "basketball", "tennis", "baseball", "cricket", "ruby", "topaz", "diamond"], "ncluster": 5, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}

data/retrieval_individual-3d6410bd-df84-4241-a31a-72548a10ef27.jsonl

@@ -30,3 +30,5 @@
 {"tstamp": 1734940672.7961, "task_type": "retrieval", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1734940670.6377, "finish": 1734940672.7961, "ip": "", "conv_id": "b0fe7fee036c4a529a178ba30594cb0f", "model_name": "Salesforce/SFR-Embedding-2_R", "prompt": "kalman filter", "output": [["kalman filter", "Kalman filter\n\nTechnical description and context The Kalman filter is an efficient recursive filter estimating the internal state of a linear dynamic system from a series of noisy measurements.  It is used in a wide range of engineering and econometric applications from radar and computer vision to estimation of structural macroeconomic models, and is an important topic in control theory and control systems engineering.  Together with the linear-quadratic regulator (LQR), the Kalman filter solves the linear–quadratic–Gaussian control problem (LQG).  The Kalman filter, the linear-quadratic regulator, and the linear–quadratic–Gaussian controller are solutions to what arguably are the most fundamental problems of control theory.\nIn most applications, the internal state is much larger (has more degrees of freedom) than the few \"observable\" parameters which are measured.  However, by combining a series of measurements, the Kalman filter can estimate the entire internal state.\nFor the Dempster–Shafer theory, each state equation or observation is considered a special case of a linear belief function and the Kalman filtering is a special case of combining linear belief functions on a join-tree or Markov tree. Additional methods include belief filtering which use Bayes or evidential updates to the state equations.\nA wide variety of Kalman filters exists by now: Kalman's original formulation - now termed the \"simple\" Kalman filter, the Kalman–Bucy filter, Schmidt's \"extended\" filter, the information filter, and a variety of \"square-root\" filters that were developed by Bierman, Thornton, and many others. Perhaps the most commonly used type of very simple Kalman filter is the phase-locked loop, which is now ubiquitous in radios, especially frequency modulation (FM) radios, television sets, satellite communications receivers, outer space communications systems, and nearly any other electronic communications equipment."]], "corpus": "wikipedia"}
 {"tstamp": 1734940766.6808, "task_type": "retrieval", "type": "chat", "model": "BM25", "gen_params": {}, "start": 1734940763.8652, "finish": 1734940766.6808, "ip": "", "conv_id": "fb78aee8ab6c4b278c30e853b6d8a150", "model_name": "BM25", "prompt": "kalman filter limitations", "output": [["kalman filter limitations", "Kalman filter\n\nNonlinear filters The basic Kalman filter is limited to a linear assumption. More complex systems, however, can be nonlinear. The nonlinearity can be associated either with the process model or with the observation model or with both.\nThe most common variants of Kalman filters for non-linear systems are the Extended Kalman Filter and Unscented Kalman filter. The suitability of which filter to use depends on the non-linearity indices of the process and observation model.\nExtended Kalman filter\nIn the extended Kalman filter (EKF), the state transition and observation models need not be linear functions of the state but may instead be nonlinear functions. These functions are of differentiable type.\nThe function f can be used to compute the predicted state from the previous estimate and similarly the function h can be used to compute the predicted measurement from the predicted state. However, f and h cannot be applied to the covariance directly. Instead a matrix of partial derivatives (the Jacobian) is computed.\nAt each timestep the Jacobian is evaluated with current predicted states. These matrices can be used in the Kalman filter equations. This process essentially linearizes the nonlinear function around the current estimate.\nUnscented Kalman filter\nWhen the state transition and observation models—that is, the predict and update functions  and —are highly nonlinear, the extended Kalman filter can give particularly poor performance."]], "corpus": "wikipedia"}
 {"tstamp": 1734940766.6808, "task_type": "retrieval", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1734940763.8652, "finish": 1734940766.6808, "ip": "", "conv_id": "f19c89ebb396405ebb5d416ca0bf5ce5", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": "kalman filter limitations", "output": [["kalman filter limitations", "Kalman filter\n\nFor statistics and control theory, Kalman filtering, also known as linear quadratic estimation, is an algorithm that uses a series of measurements observed over time, including statistical noise and other inaccuracies, and produces estimates of unknown variables that tend to be more accurate than those based on a single measurement alone, by estimating a joint probability distribution over the variables for each timeframe. The filter is constructed as a mean squared error minimiser, but an alternative derivation of the filter is also provided showing how the filter relates to maximum likelihood statistics. The filter is named after Rudolf E. Kálmán, who was one of the primary developers of its theory.\nThis digital filter is sometimes termed the Stratonovich–Kalman–Bucy filter because it is a special case of a more general, nonlinear filter developed somewhat earlier by the Soviet mathematician Ruslan Stratonovich. In fact, some of the special case linear filter's equations appeared in  papers by Stratonovich that were published before the summer of 1961, when Kalman met with Stratonovich during a conference in Moscow."]], "corpus": "wikipedia"}
+{"tstamp": 1734943351.7352, "task_type": "retrieval", "type": "chat", "model": "embed-english-v3.0", "gen_params": {}, "start": 1734943351.34, "finish": 1734943351.7352, "ip": "", "conv_id": "41a8478744724bdf87fc59b77b087b99", "model_name": "embed-english-v3.0", "prompt": "where does fungi go on a food web", "output": [["where does fungi go on a food web", "Fungivore\n\nAnts Around 220 described  species, and more undescribed species of ants in the tribe Attini cultivate fungi. They are only found in the New World and are thought to have evolved in the Amazon Rainforest, where they are most diverse today. For these ants, farmed fungi are the only source of food on which their larvae are raised on and are also an important food for adults. Queen ants carry a small part of fungus in small pouches in their mouthparts when they leave the nest to mate, allowing them to establish a new fungus garden when they form a new nest. Different lineages cultivate fungi on different substrates, those that evolved earlier do so on a wide range of plant matter, whereas leaf cutter ants are more selective, mainly using only fresh leaves and flowers. The fungi are members of the families Lepiotaceae and Pterulaceae. Other fungi in the genus Escovopsis parasitise the gardens and antibiotic-producing bacteria also inhabit the gardens.\nHumans\nGastropods\nThe marine snail Littoraria irrorata, which lives in the salt marshes of the southeast of the United States feeds on fungi that it encourages to grow. It creates and maintains wounds on the grass, Spartina alterniflora which are then infected by fungi, probably of the genera Phaeosphaeria and Mycosphaerella, which are the preferred diet of the snail. They also deposit faeces on the wounds that they create, which encourage the growth of the fungi because they are rich in nitrogen and fungal hyphae. Juvenile snails raised on uninfected leaves do not grow and are more likely to die, indicating the importance of the fungi in the diet of L. irrorata."]], "corpus": "wikipedia"}
+{"tstamp": 1734943351.7352, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1734943351.34, "finish": 1734943351.7352, "ip": "", "conv_id": "0e1159d4327e4c0c8ba76339b404ffbf", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "where does fungi go on a food web", "output": [["where does fungi go on a food web", "Marine fungi\n\nDifferent marine habitats support very different fungal communities. Fungi can be found in niches ranging from ocean depths and coastal waters to mangrove swamps and estuaries with low salinity levels. Marine fungi can be saprobic or parasitic on animals, saprobic or parasitic on algae, saprobic on plants or saprobic on dead wood.\nOverview\nTerrestrial fungi play critical roles in nutrient cycling and food webs and can shape macroorganism communities as parasites and mutualists. Although estimates for the number of fungal species on the planet range from 1.5 to over 5 million, likely fewer than 10% of fungi have been identified so far. To date, a relatively small percentage of described species are associated with marine environments, with ~1,100 species retrieved exclusively from the marine environment. Nevertheless, fungi have been found in nearly every marine habitat explored, from the surface of the ocean to kilometers below ocean sediments. Fungi are hypothesized to contribute to phytoplankton population cycles and the biological carbon pump and are active in the chemistry of marine sediments. Many fungi have been identified as commensals or pathogens of marine animals (e.g., corals and sponges), plants, and algae. Despite their varied roles, remarkably little is known about the diversity of this major branch of eukaryotic life in marine ecosystems or their ecological functions."]], "corpus": "wikipedia"}