{"id":"W2317471821","doi":"10.1021/es301320n","title":"Negative Consequences of Using α = 0.05 for Environmental Monitoring Decisions: A Case Study from a Decade of Canada’s Environmental Effects Monitoring Program","year":2012,"lang":"en","type":"article","venue":"Environmental Science & Technology","topic":"Reliability and Agreement in Measurement","field":"Decision Sciences","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Water Network; University of New Brunswick","funders":"Natural Resources Canada; Canadian Forest Service; Natural Sciences and Engineering Research Council of Canada; Canadian Rivers Institute, University of New Brunswick","keywords":"Type I and type II errors; Statistics; Null hypothesis; Set (abstract data type); Econometrics; Approximation error; Mathematics; Computer science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.1180571,0.0005247759,0.0006900616,0.001968005,0.01003097,0.003415445,0.002770951,0.002518012,0.0006284884],"category_scores_gemma":[0.2394375,0.0004576363,0.0008922016,0.004969155,0.004833007,0.001826278,0.001632546,0.004071545,0.00008020387],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.03334803,"about_ca_system_score_gemma":0.05817337,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7753841,"about_ca_topic_score_gemma":0.9005197,"domain_scores_codex":[0.9025332,0.05368305,0.00418751,0.003506972,0.02954691,0.006542353],"domain_scores_gemma":[0.5428464,0.3577361,0.01989029,0.01164911,0.06277198,0.005106026],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001891491,0.001082175,0.557637,0.0009592382,0.0006869698,0.008528025,0.0611599,0.03159728,0.004277553,0.0401315,0.02391016,0.2681386],"study_design_scores_gemma":[0.0004047205,0.001985527,0.732942,0.001936165,0.0008466673,0.002835858,0.08094446,0.05376593,0.01295225,0.03530727,0.07543705,0.0006420594],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9291195,0.001375056,0.01732461,0.01697195,0.0001538212,0.0005870109,0.0002800439,0.000115533,0.03407247],"genre_scores_gemma":[0.9809635,0.0002610409,0.01568314,0.002012082,0.00001629374,0.0001435006,0.00005378722,0.00002967185,0.000837107],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.966652,"threshold_uncertainty_score":0.6243532,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08694077720667744,"score_gpt":0.3592435348098463,"score_spread":0.2723027576031689,"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."}}