{"id":"W4403762045","doi":"10.1089/ast.2024.0019","title":"A Machine-Learning Approach to Biosignature Exploration on Early Earth and Mars Using Sulfur Isotope and Trace Element Data in Pyrite","year":2024,"lang":"en","type":"article","venue":"Astrobiology","topic":"Paleontology and Stratigraphy of Fossils","field":"Earth and Planetary Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Mars Exploration Program; Astrobiology; Pyrite; Trace element; Sulfur; Earth (classical element); Exploration of Mars; Martian; Geology; TRACE (psycholinguistics); Earth science; Mineralogy; Geochemistry; Materials science; Metallurgy; Philosophy; Physics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0007721544,0.0004570004,0.0004213393,0.001746837,0.0004352684,0.0007717464,0.0007899677,0.000653676,0.0002667069],"category_scores_gemma":[0.001550845,0.0002672338,0.0005523494,0.000770182,0.0004569743,0.0005865227,0.0004057987,0.0004485428,0.0001226312],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006353459,"about_ca_system_score_gemma":0.0004948055,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004587939,"about_ca_topic_score_gemma":0.006100927,"domain_scores_codex":[0.9996693,0.00009657004,0.00001819526,0.0001249221,0.00006521717,0.00002582499],"domain_scores_gemma":[0.9995053,0.0002069506,0.00009940202,0.00004654847,0.0001126109,0.00002920929],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001606775,0.0002979286,0.07968962,0.0001299041,0.0002650892,0.0002014106,0.0002288385,0.6262115,0.03127961,0.0040851,0.0006338248,0.2568165],"study_design_scores_gemma":[0.000004173461,0.0000294441,0.005135855,0.0000044474,0.000009199368,0.00003136515,0.00002591575,0.9895868,0.002527572,0.002323354,0.0003123379,0.00000945469],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4467965,0.0005881003,0.548858,0.0004464748,0.00002423896,0.00008160818,0.00028468,0.000769049,0.00215129],"genre_scores_gemma":[0.8630579,0.0001230189,0.135897,0.00004375157,0.00003195285,0.00006101173,0.0001780193,0.0000219454,0.0005852656],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004587939,"threshold_uncertainty_score":0.009122491,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03573457719806641,"score_gpt":0.2493547067750732,"score_spread":0.2136201295770068,"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."}}