{"id":"W4388006129","doi":"10.1007/s11053-023-10273-6","title":"Predictive Geochemical Exploration: Inferential Generation of Modern Geochemical Data, Anomaly Detection and Application to Northern Manitoba","year":2023,"lang":"en","type":"article","venue":"Natural Resources Research","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Geological Survey of Canada","funders":"Natural Resources Canada","keywords":"Data integration; Anomaly detection; Pipeline (software); Data quality; Analytics; Computer science; Data mining; Geospatial analysis; Mineral exploration; Data science; Earth science; Geology; Geochemistry; Engineering; Remote sensing","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.001138338,0.0004550081,0.0001659813,0.0009709816,0.0009696928,0.0008656151,0.0008848998,0.000312123,0.001079451],"category_scores_gemma":[0.002724431,0.0001957021,0.0003613135,0.002262172,0.0008407067,0.000425579,0.0007905685,0.0004112371,0.0001487712],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006085904,"about_ca_system_score_gemma":0.009608295,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.872811,"about_ca_topic_score_gemma":0.9201174,"domain_scores_codex":[0.9996054,0.000110445,0.00001677017,0.0000815794,0.0001394623,0.00004646512],"domain_scores_gemma":[0.9987131,0.0005032058,0.00008000401,0.0001355354,0.0004809997,0.00008717012],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00048426,0.0005306482,0.4228545,0.00031978,0.0001677468,0.001991529,0.004404814,0.3135355,0.01127659,0.005859834,0.007699782,0.2308751],"study_design_scores_gemma":[0.0001050612,0.0001591967,0.1866654,0.00008048838,0.00006407302,0.0001493474,0.004840999,0.7781913,0.008223214,0.003852877,0.01758924,0.00007865762],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9729918,0.0002158468,0.01463428,0.001211447,0.00001639364,0.0002251954,0.002640838,0.001667269,0.006396969],"genre_scores_gemma":[0.9509258,0.0002705947,0.04453373,0.0001100501,0.000007827633,0.00005644879,0.002471388,0.00007363722,0.00155056],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.127189,"threshold_uncertainty_score":0.2558761,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08495568577304716,"score_gpt":0.3304075598028264,"score_spread":0.2454518740297793,"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."}}