{"id":"W2792168653","doi":"10.1071/aseg2018abt7_3g","title":"The Utility of Machine Learning in Identification of Key Geophysical and Geochemical Datasets: A Case Study in Lithological Mapping in the Central African Copper Belt","year":2018,"lang":"en","type":"article","venue":"ASEG Extended Abstracts","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"First Quantum Minerals","keywords":"Ranking (information retrieval); Random forest; Lithology; Geologic map; Workflow; Geology; Identification (biology); Variable (mathematics); Machine learning; Data mining; Artificial intelligence; Computer science; Database; Paleontology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002007832,0.0001244574,0.0002107594,0.00005619031,0.00009981024,0.00004689086,0.0005414685,0.00006927735,0.000003454451],"category_scores_gemma":[0.0008463635,0.00008117859,0.0000281775,0.0004393759,0.0003067564,0.0001519107,0.0002777929,0.0004202752,9.081597e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001587368,"about_ca_system_score_gemma":0.00003709978,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001441678,"about_ca_topic_score_gemma":0.0009268408,"domain_scores_codex":[0.9981105,0.0003423456,0.0006084786,0.0003892995,0.0002279465,0.0003214016],"domain_scores_gemma":[0.9986311,0.0005575294,0.0002241343,0.0004916029,0.00005218594,0.00004346021],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005162918,0.01233274,0.6147102,0.000379037,0.00009932315,0.009435032,0.09703909,0.0004365992,0.04019011,0.005375215,0.0004898048,0.2189966],"study_design_scores_gemma":[0.000430839,0.00008644151,0.9786345,0.00002275662,0.000003762716,0.0002070098,0.004243311,0.01234496,0.002119581,0.001699009,0.0001195682,0.00008826304],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9984575,0.00005385982,0.0000906374,0.0007732549,0.00002696817,0.0002721482,0.00001393243,0.00001170959,0.0002999957],"genre_scores_gemma":[0.99972,0.0000060466,0.0001891288,0.00002389042,0.00001876796,0.00001641709,0.00001882265,0.000001440173,0.000005488457],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3639243,"threshold_uncertainty_score":0.3310367,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02321311985908564,"score_gpt":0.2706567366403371,"score_spread":0.2474436167812515,"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."}}