{"id":"W4407956463","doi":"10.1007/s12145-025-01811-2","title":"Rotation-based outlier detection for geochemical anomaly identification in stream sediment multivariate data","year":2025,"lang":"en","type":"article","venue":"Earth Science Informatics","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"James Cook University","keywords":"Multivariate statistics; Anomaly (physics); Outlier; Anomaly detection; Sediment; Geology; Identification (biology); Rotation (mathematics); Multivariate analysis; Data mining; Computer science; Artificial intelligence; Geomorphology; Physics; Machine learning","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":[0.001440332,0.00009523937,0.00009821363,0.0002173895,0.0002271863,0.0002848794,0.001366572,0.00005379703,0.000003593254],"category_scores_gemma":[0.0008709807,0.00009128977,0.00002116952,0.001069346,0.0001204528,0.001409452,0.0003005253,0.00009936906,0.00001149757],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000451885,"about_ca_system_score_gemma":0.0003408034,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002972954,"about_ca_topic_score_gemma":0.00002715005,"domain_scores_codex":[0.9986879,0.00001249785,0.0004709238,0.0002653652,0.0002834994,0.0002798508],"domain_scores_gemma":[0.9985758,0.0001356393,0.0001723664,0.000835822,0.0002297681,0.00005058029],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009846489,0.0008031732,0.02478249,0.001124331,0.00004532751,0.000004206057,0.01027547,0.1908711,0.2579269,0.02521103,0.001115684,0.4877418],"study_design_scores_gemma":[0.0003188792,0.00001387202,0.008000385,0.0000275997,0.000002596383,6.616779e-7,0.0001428368,0.86467,0.1236766,0.001098896,0.001960081,0.00008760481],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08988339,0.000004647594,0.9069654,0.0009252239,0.0003084078,0.0003816376,0.00000970977,0.0000662081,0.001455332],"genre_scores_gemma":[0.929461,5.007247e-7,0.07005501,0.0002123179,0.00001202125,0.00003578011,0.0000336587,7.249876e-7,0.0001889949],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8395776,"threshold_uncertainty_score":0.3722689,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02164965765759234,"score_gpt":0.282091829598641,"score_spread":0.2604421719410486,"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."}}