{"id":"W4389256095","doi":"10.2138/am-2023-9115","title":"Machine learning applied to apatite compositions for determining mineralization potential","year":2023,"lang":"en","type":"article","venue":"American Mineralogist","topic":"Mineralogy and Gemology Studies","field":"Earth and Planetary Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"Fundamental Research Funds for the Central Universities; China University of Geosciences; China University of Geosciences, Beijing; National Natural Science Foundation of China","keywords":"Apatite; Mineralization (soil science); Geochemistry; Geology; Mineralogy; Soil science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001537464,0.0001682592,0.0002663722,0.0002060131,0.0004941691,0.00003321735,0.0001478987,0.00003871713,0.0001586752],"category_scores_gemma":[0.00009691947,0.0001520302,0.0000647663,0.0006398245,0.0002673639,0.00005600841,0.00002399874,0.0001022449,0.0003320037],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003978773,"about_ca_system_score_gemma":0.00001266336,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003324335,"about_ca_topic_score_gemma":0.00838411,"domain_scores_codex":[0.9988617,0.00006044715,0.0002235085,0.0003357863,0.0001103499,0.0004082038],"domain_scores_gemma":[0.9994166,0.0002075507,0.00009983091,0.0001263026,0.00004486723,0.0001047813],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002289101,0.00002991727,0.5953251,0.00002736309,0.0001181807,0.00005737437,0.0008552424,0.3033209,0.006323204,0.0007265172,0.02531049,0.06767686],"study_design_scores_gemma":[0.0007831691,0.0009320723,0.8370886,0.00001278563,0.00007767406,0.00004088671,0.0005208139,0.1333995,0.00007960151,0.0004812468,0.02597059,0.0006130616],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9619823,0.00008978364,0.02080871,0.003683136,0.0004984158,0.0006426401,0.0003822358,0.0004573407,0.01145549],"genre_scores_gemma":[0.9894238,0.00002890935,0.005125062,0.001285681,0.0001982616,0.00003099998,0.002033828,0.000008563288,0.001864959],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2417635,"threshold_uncertainty_score":0.6199611,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01752493412984343,"score_gpt":0.252080457997479,"score_spread":0.2345555238676355,"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."}}