{'fraction_non_alphanumeric': 0.026737967914438502, 'fraction_numerical': 0.01871657754010695, 'mean_word_length': 5.139344262295082, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Probing Venus Surface Iron Contents With Six‐Band Visible Near‐Infrared Spectroscopy From Orbit

Machine learning models enable interpretation of orbital spectral measurements of Venus using laboratory calibration data collected at Venus surface temperatures. Partial least squares models show that total iron content can be accurately predicted using data from the six bands (two in the 1.02 μm window). Prediction errors on total wt% FeO are ±0.50 for common subalkaline volcanic rocks. Accuracy is ±0.42 for wt% FeO in alkaline rocks, and ±2.47 for all 18 igneous samples studied to date. These robust capabilities will allow discrimination of basalt versus rhyolite/granite and elucidate the rock type of the enigmatic tessera terrain on Venus.