{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004545012,0.00002570343,0.00004287435,0.00002404975,0.0002329549,0.00002607347,0.00004990851,0.000005604887,0.000001773823],"category_scores_gemma":[0.00001058703,0.00001747008,0.000008510813,0.0001824642,0.0003365389,0.0002318095,0.00001656733,0.00001664071,0.000001153298],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":6.489353e-7,"about_ca_system_score_gemma":0.00002803614,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009908675,"about_ca_topic_score_gemma":0.00005066568,"domain_scores_codex":[0.9996584,0.000008376083,0.00005693848,0.00008507069,0.00009728084,0.00009392444],"domain_scores_gemma":[0.9997818,0.00005384491,0.00004096833,0.00005348634,0.00004098613,0.00002894495],"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":[0.0001951881,0.00009979343,0.3479949,0.0001490701,0.0000136764,2.782609e-7,0.006605898,0.0002382414,0.3973137,0.1669186,0.001465615,0.07900509],"study_design_scores_gemma":[0.0001480704,0.00009229462,0.8155668,0.00001264593,0.000005326879,2.261131e-7,0.001149874,0.1416207,0.03405688,0.006773774,0.0005258247,0.00004756032],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9982671,0.00001833927,0.0007507543,0.0005150955,0.00004209996,0.0001052349,0.00003210497,0.000003443621,0.0002658841],"genre_scores_gemma":[0.9996913,0.00001005853,0.0002163043,0.000003202271,0.000009357361,0.000002353715,0.00002063913,4.670128e-7,0.00004631069],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4675719,"threshold_uncertainty_score":0.1791725,"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."}}