{"id":"W4321238839","doi":"10.1002/env.2788","title":"Environmental data science: Part 2","year":2023,"lang":"en","type":"article","venue":"Environmetrics","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada; Trent University","funders":"Agriculture and Agri-Food Canada","keywords":"Data science; Field (mathematics); Environmental research; Computer science; Set (abstract data type); Discipline; Environmental data; Sampling (signal processing); Citizen science; Management science; Engineering ethics; Sociology; Environmental science; Environmental resource management; Political science; Mathematics; Social science; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01118712,0.001149522,0.001156498,0.004762384,0.001967719,0.01037128,0.001749349,0.004646257,0.03388268],"category_scores_gemma":[0.03109981,0.000680993,0.001241894,0.004125302,0.003304185,0.005706962,0.003169209,0.008093183,0.0189815],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003980415,"about_ca_system_score_gemma":0.007283808,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003523534,"about_ca_topic_score_gemma":0.003451159,"domain_scores_codex":[0.9909583,0.002006416,0.0009649624,0.001335755,0.004447255,0.0002872214],"domain_scores_gemma":[0.9657614,0.01247798,0.001821111,0.002692548,0.01499,0.00225704],"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":[0.000007852947,0.000008973283,0.00007175653,0.0002646131,0.000009657863,0.00003245493,0.00004964877,0.0001143106,0.0001321018,0.0098977,0.9560894,0.0333214],"study_design_scores_gemma":[0.000001867974,0.00000635849,0.0001676906,0.0002504611,0.00000325085,0.00003548917,0.00001890841,0.00006017834,0.00003855882,0.003593457,0.9958171,0.000006642468],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"empirical","genre_scores_codex":[0.0003230262,0.09084275,0.009718345,0.1939932,0.6544752,0.0001787457,0.001815621,0.0004067534,0.04824628],"genre_scores_gemma":[0.00706225,0.09353462,0.006457996,0.1178496,0.6469629,0.0003620585,0.003043038,0.0008269704,0.1239006],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03388268,"threshold_uncertainty_score":0.1133488,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02412484094849081,"score_gpt":0.2372008081013267,"score_spread":0.2130759671528359,"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."}}