{"id":"W4319789100","doi":"10.1002/env.2787","title":"Environmental data science: Part 1","year":2023,"lang":"en","type":"article","venue":"Environmetrics","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Trent University; Agriculture and Agri-Food Canada","funders":"","keywords":"Data science; Field (mathematics); Environmental research; Computer science; Focus (optics); Set (abstract data type); Climate science; Discipline; Environmental data; Management science; Climate change; Sociology; Environmental resource management; Mathematics; Ecology; Environmental science; Social science; Engineering","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00116555,0.0003064604,0.0002157285,0.0000982542,0.000586534,0.00006433987,0.002116656,0.0001185489,0.006280448],"category_scores_gemma":[0.0001390261,0.0003023921,0.00006291489,0.002880386,0.00209496,0.001192981,0.004499554,0.0002712207,0.01900699],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005848214,"about_ca_system_score_gemma":0.00001522781,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006623275,"about_ca_topic_score_gemma":0.000003600367,"domain_scores_codex":[0.9960358,0.00003996191,0.0003542192,0.001192442,0.001434424,0.0009431337],"domain_scores_gemma":[0.9974503,0.00009032117,0.0001192778,0.001953664,6.797344e-7,0.0003857723],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002172417,0.000466365,0.6412913,0.000007640139,0.00003327528,0.0001377603,0.000339239,0.08477152,0.01110598,0.0001577375,0.04733165,0.2143358],"study_design_scores_gemma":[0.0003476831,0.00006659301,0.5147881,0.000002909436,0.00002952079,0.00002060021,0.0003036655,0.04382319,0.0003059753,0.000213106,0.4395491,0.0005495648],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.981784,0.00008749367,0.002180568,0.0002095675,0.0005930816,0.0003027961,0.0001159786,0.0003264002,0.01440015],"genre_scores_gemma":[0.9835495,0.001529744,0.00748245,0.0003976827,0.0001512706,0.00002094585,0.0003014159,0.00007955579,0.006487418],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3922174,"threshold_uncertainty_score":0.9999428,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02519190215816607,"score_gpt":0.2378192430411737,"score_spread":0.2126273408830077,"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."}}