{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001921369,0.0009104736,0.000585797,0.00316073,0.0003185387,0.000959634,0.0006045157,0.0008122409,0.001132992],"category_scores_gemma":[0.0036232,0.0001826907,0.0007875917,0.00121309,0.0002787973,0.0006554167,0.0005907608,0.000692157,0.0005851882],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006995791,"about_ca_system_score_gemma":0.0005613267,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003949571,"about_ca_topic_score_gemma":0.002180968,"domain_scores_codex":[0.9992573,0.0002400596,0.00006524235,0.0002026753,0.0001637673,0.00007085787],"domain_scores_gemma":[0.998372,0.0009848099,0.0001853165,0.00009205698,0.0003149566,0.00005094401],"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":[0.000427681,0.0004477711,0.1377917,0.0002314456,0.0003466293,0.0001658547,0.0001232626,0.4175976,0.02310756,0.001014156,0.001570344,0.4171759],"study_design_scores_gemma":[0.00000511345,0.00004107337,0.007256062,0.00001182528,0.00001648105,0.0000255967,0.00002138215,0.9886651,0.003258353,0.0004751053,0.0002170054,0.000006949982],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6931111,0.001170255,0.2996347,0.0002481962,0.00006675514,0.0001256815,0.0007677876,0.001849983,0.003025611],"genre_scores_gemma":[0.9707427,0.00009545048,0.02797738,0.00002787206,0.00001649493,0.00004441502,0.0004350968,0.00001664922,0.0006439612],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003949571,"threshold_uncertainty_score":0.01016128,"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."}}