{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001160087,0.0003063767,0.0002157739,0.00009819894,0.0005865709,0.00006445982,0.002115646,0.0001185494,0.006179269],"category_scores_gemma":[0.0001375833,0.0003023072,0.00006290444,0.002878468,0.002094935,0.001193682,0.004499013,0.0002711671,0.01883592],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005849653,"about_ca_system_score_gemma":0.00001516957,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006680575,"about_ca_topic_score_gemma":0.000003618667,"domain_scores_codex":[0.9960371,0.00003995033,0.0003543561,0.001191677,0.001434118,0.0009428367],"domain_scores_gemma":[0.9974545,0.00008922376,0.0001194217,0.001950455,6.784385e-7,0.0003857256],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000218642,0.0004714457,0.6329671,0.000007785655,0.00003364748,0.0001425691,0.0003432795,0.08606761,0.01089593,0.000161909,0.04718054,0.2217064],"study_design_scores_gemma":[0.0003480978,0.00006629229,0.5075126,0.000002865156,0.0000292918,0.0000205433,0.0002995986,0.04337399,0.0003029217,0.0002135629,0.4472843,0.0005460177],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9815207,0.00008881196,0.002143197,0.0002089156,0.0005968465,0.0003027373,0.0001139082,0.0003267686,0.01469811],"genre_scores_gemma":[0.9835697,0.001570541,0.007370926,0.0003992819,0.0001538022,0.0000206822,0.0003037167,0.00007977599,0.006531578],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4001037,"threshold_uncertainty_score":0.9999429,"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."}}