{"id":"W4391649631","doi":"10.1016/j.gexplo.2024.107416","title":"Exploratory functional data analysis of multivariate densities for the identification of agricultural soil contamination by risk elements","year":2024,"lang":"en","type":"article","venue":"Journal of Geochemical Exploration","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Austrian Science Fund; Grantová Agentura České Republiky; Deutsche Forschungsgemeinschaft","keywords":"Multivariate statistics; Cluster analysis; Compositional data; Soil water; Contamination; Exploratory data analysis; Principal component analysis; Environmental science; Agriculture; Soil science; Computer science; Data mining; Statistics; Geography; Mathematics; Ecology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001586627,0.00008878607,0.000205578,0.0001182506,0.00007412562,0.00007687201,0.0004794469,0.0000612035,0.000007460042],"category_scores_gemma":[0.0007574166,0.00005851488,0.000141161,0.0005530661,0.00004976629,0.001264721,0.000103106,0.0001283257,6.879247e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003526309,"about_ca_system_score_gemma":0.000066022,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007208346,"about_ca_topic_score_gemma":0.000005091003,"domain_scores_codex":[0.9984096,0.00007817024,0.0008505217,0.0001936555,0.0003770417,0.00009103871],"domain_scores_gemma":[0.9973814,0.0004888332,0.0009415509,0.0003016261,0.0008584051,0.00002816678],"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":[0.00005296649,0.0001216658,0.0003189042,0.0001009664,0.001227269,6.650873e-7,0.001782537,0.01431681,0.9656055,0.002368196,0.00880916,0.00529537],"study_design_scores_gemma":[0.0003243582,0.00006379428,0.004554589,0.00005167375,0.0008477934,0.000004569563,0.001179927,0.6162571,0.3722072,0.003008513,0.001397236,0.0001032231],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1725364,0.0004727129,0.8249515,0.001518238,0.000309674,0.0001024514,0.00008590236,0.00001209134,0.00001106883],"genre_scores_gemma":[0.9977247,0.0001009338,0.001608347,0.00001261412,0.0001002619,0.00001063475,0.0003586331,0.000001670134,0.00008225584],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8251883,"threshold_uncertainty_score":0.2386168,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03916403732834565,"score_gpt":0.2692422219015214,"score_spread":0.2300781845731757,"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."}}