{"id":"W1582174410","doi":"10.1002/ieam.1636","title":"Development of an adaptive monitoring framework for long-term programs: An example using indicators of fish health","year":2015,"lang":"en","type":"article","venue":"Integrated Environmental Assessment and Management","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada’s Oil Sands Innovation Alliance; University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Scale (ratio); Range (aeronautics); Baseline (sea); Fish <Actinopterygii>; Environmental resource management; Term (time); Computer science; Environmental science; Statistics; Geography; Cartography; Fishery; Mathematics; Biology; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006036471,0.0002651026,0.0003457377,0.0001231124,0.0002139586,0.00002063468,0.000257121,0.00008034576,0.00006741293],"category_scores_gemma":[0.000002139668,0.0002471381,0.00004204768,0.0001762663,0.000281986,0.0003637905,0.0004570711,0.000115477,0.000002159019],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000607006,"about_ca_system_score_gemma":0.00002369505,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001466986,"about_ca_topic_score_gemma":0.0004409306,"domain_scores_codex":[0.9981515,0.00007993564,0.0005117447,0.000506251,0.0003763048,0.0003742995],"domain_scores_gemma":[0.9991457,0.00002050881,0.0003629407,0.0002671999,0.000005376768,0.0001982489],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002173988,0.002484177,0.7781953,0.0001985354,0.0005167944,0.000008921581,0.005236814,0.0004128128,0.0002545791,0.001246514,0.0002246223,0.2110036],"study_design_scores_gemma":[0.001163717,0.001662977,0.9720695,0.0001572939,0.0001210241,0.000001371791,0.02034203,0.0006958664,0.00117295,0.0006917603,0.001527416,0.0003940617],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.968734,0.00002202071,0.029284,0.00004452726,0.0001445156,0.001350741,0.00001224517,0.00003443412,0.0003734857],"genre_scores_gemma":[0.7719996,0.0000870783,0.2274724,0.00009593618,0.00001460853,0.0001733113,0.00009303175,0.00002068692,0.0000433078],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2106095,"threshold_uncertainty_score":0.9999981,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06906098495274408,"score_gpt":0.3235726419525954,"score_spread":0.2545116569998513,"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."}}