{"id":"W4413897410","doi":"10.1007/s00126-025-01388-2","title":"Trace element composition of chalcopyrite as a tool for deposit type discrimination from magmatic and hydrothermal settings: a machine learning approach","year":2025,"lang":"en","type":"article","venue":"Mineralium Deposita","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Chicoutimi; Université Laval","funders":"","keywords":"Chalcopyrite; Hydrothermal circulation; Geology; Geochemistry; Mineral resource classification; Trace element; TRACE (psycholinguistics); Mineralogy; Metallurgy; Copper; Paleontology; Materials 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":[0.0002023644,0.0001441895,0.0001996935,0.00006369028,0.0001394548,0.00008629511,0.0002511281,0.00006750762,0.000004401579],"category_scores_gemma":[0.00005202434,0.0001365124,0.0000492468,0.0001801529,0.00002789584,0.0001603039,0.0001356304,0.00009953394,0.000001016284],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000232873,"about_ca_system_score_gemma":0.00001535783,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008738386,"about_ca_topic_score_gemma":0.00002766041,"domain_scores_codex":[0.9989982,0.00006917586,0.0002849103,0.0003370233,0.0001287542,0.0001819336],"domain_scores_gemma":[0.999438,0.00009542609,0.000129842,0.0002159864,0.00008942221,0.00003126409],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002063211,0.0004759169,0.02767634,0.002024201,0.0002566087,0.00001259092,0.005285467,0.004823488,0.9234889,0.02046022,0.0006070092,0.01468296],"study_design_scores_gemma":[0.001092405,0.0002049428,0.01390394,0.0002601201,0.0001055999,0.0000300595,0.00009366194,0.9337731,0.04768214,0.002228006,0.000356908,0.0002691153],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6737925,0.0007778105,0.3202652,0.002018989,0.00006414254,0.0003923558,0.000006253771,0.00006195311,0.002620757],"genre_scores_gemma":[0.9466308,0.000008377841,0.05221841,0.0001637392,0.00002380813,0.00005091556,0.00009593696,0.000003651169,0.0008043758],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9289496,"threshold_uncertainty_score":0.5566812,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009301273358020556,"score_gpt":0.2305984679480961,"score_spread":0.2212971945900755,"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."}}