{"id":"W4313679430","doi":"10.1029/2022ea002710","title":"The Utility of RGB Color for Discrimination of Lunar Maturity and Composition","year":2023,"lang":"en","type":"article","venue":"Earth and Space Science","topic":"Planetary Science and Exploration","field":"Physics and Astronomy","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Winnipeg; Reach Technologies (Canada)","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Space Agency; Canada Foundation for Innovation; National Natural Science Foundation of China; University of Winnipeg; National Aeronautics and Space Administration; National Science Foundation","keywords":"RGB color model; Maturity (psychological); Multispectral image; Colorimetry; Sample (material); Spectroscopy; Materials science; Geology; Analytical Chemistry (journal); Mineralogy; Remote sensing; Optics; Computer science; Artificial intelligence; Chemistry; Physics; Environmental chemistry","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003594583,0.0002216109,0.0001109135,0.0007399322,0.00009030734,0.0004629918,0.0001623842,0.0001688919,0.0008165661],"category_scores_gemma":[0.0005936504,0.000111455,0.0001519129,0.0003706863,0.0001519969,0.0002722462,0.000154278,0.0001461916,0.0002890341],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009250534,"about_ca_system_score_gemma":0.00007857279,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009372226,"about_ca_topic_score_gemma":0.001398435,"domain_scores_codex":[0.9999056,0.000027246,0.000003969208,0.00002392109,0.00002519338,0.00001413722],"domain_scores_gemma":[0.9997758,0.00008588402,0.00003478281,0.00002573505,0.00005499707,0.00002276992],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0003262513,0.0000839564,0.1642612,0.00006785387,0.0000500779,0.00009735231,0.0001451331,0.002974363,0.65648,0.001112938,0.0004359846,0.1739649],"study_design_scores_gemma":[0.00001743841,0.0004336337,0.6397911,0.00003481068,0.00009395094,0.0008133114,0.000258606,0.0651994,0.2889746,0.001146735,0.003156805,0.00007966565],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.917392,0.0005370263,0.07583045,0.00006705122,0.00001678709,0.00002691977,0.0003251154,0.0005473605,0.005257186],"genre_scores_gemma":[0.9545764,0.0001294853,0.04456294,0.00002939616,0.0000078961,0.0000120437,0.0001270529,0.00003695899,0.0005178791],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0009372226,"threshold_uncertainty_score":0.002731621,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01664379780047939,"score_gpt":0.2496202492726767,"score_spread":0.2329764514721973,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}