{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009150014,0.001127049,0.001076345,0.004583183,0.001828648,0.009206198,0.001541232,0.004077832,0.028159],"category_scores_gemma":[0.02670413,0.0006005808,0.0009225137,0.004672258,0.003352996,0.006580473,0.002896719,0.007278996,0.01659522],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003783362,"about_ca_system_score_gemma":0.006570717,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003626264,"about_ca_topic_score_gemma":0.003130205,"domain_scores_codex":[0.9928989,0.001706671,0.0008586183,0.001003779,0.003322121,0.0002099248],"domain_scores_gemma":[0.9721633,0.01065166,0.001468043,0.002107246,0.01209829,0.001511502],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000009492293,0.00001164093,0.0000982829,0.000503013,0.00001099481,0.000037118,0.00007897442,0.0001403412,0.0001502568,0.01784337,0.9306669,0.05044955],"study_design_scores_gemma":[0.000001495401,0.000006164308,0.0001695398,0.0003593359,0.000003098799,0.00003113001,0.00002297445,0.00004342846,0.00003642643,0.004786777,0.9945333,0.000006242552],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"other","genre_scores_codex":[0.0004085141,0.1896109,0.009276719,0.2223743,0.5113105,0.0002024748,0.002308828,0.0003721759,0.06413556],"genre_scores_gemma":[0.01033528,0.188091,0.007038189,0.1193953,0.5609577,0.0003427531,0.003791515,0.000735578,0.1093127],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.028159,"threshold_uncertainty_score":0.09420121,"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."}}