{"id":"W2947837746","doi":"10.1016/j.ecolind.2019.05.055","title":"Making ecological indicators management ready: Assessing the specificity, sensitivity, and threshold response of ecological indicators","year":2019,"lang":"en","type":"article","venue":"Ecological Indicators","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":62,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bedford Institute of Oceanography; Fisheries and Oceans Canada","funders":"National Oceanic and Atmospheric Administration; Centre National de la Recherche Scientifique; United Nations Educational, Scientific and Cultural Organization; National Research Foundation; Department of Science and Technology, Ministry of Science and Technology, India; Institut de Recherche pour le Développement","keywords":"Fishing; Environmental science; Marine ecosystem; Ecosystem; Ecological indicator; Biomass (ecology); Fisheries management; Trophic level; Fishery; Ecology; Environmental resource management; Overfishing; Sustainability; Biology","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.004981198,0.0004815539,0.0007385817,0.0005221515,0.0005901391,0.0002126144,0.00101327,0.0006323737,0.01897109],"category_scores_gemma":[0.0004569899,0.0003084678,0.0001959897,0.00188329,0.00228693,0.0003155436,0.003954229,0.001187977,0.0004802046],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00042555,"about_ca_system_score_gemma":0.00005154176,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002240835,"about_ca_topic_score_gemma":0.00003843625,"domain_scores_codex":[0.9943232,0.001320582,0.0008466912,0.001190043,0.001182723,0.001136791],"domain_scores_gemma":[0.9963985,0.001908544,0.0005256011,0.0007848325,0.00001388771,0.0003686546],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002146339,0.0008190786,0.9832944,0.00002973583,0.00007174174,0.0003072928,0.0001561807,0.00004197071,0.0004217638,0.002937687,0.001319261,0.01038626],"study_design_scores_gemma":[0.0004948243,0.0006593357,0.9711747,0.00001181757,0.00003876778,0.00003511784,0.0006772638,0.0002077301,0.0002442507,0.0005764714,0.02548015,0.0003995384],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8539025,0.0000115399,0.00001936778,0.0009679951,0.0001745476,0.001076082,0.00001261143,0.0000989637,0.1437364],"genre_scores_gemma":[0.9972564,0.00009275151,0.0005648948,0.0009078468,0.0000595095,0.00008401398,0.00001303874,0.0000327698,0.0009887657],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1433539,"threshold_uncertainty_score":0.9999368,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03502620992867532,"score_gpt":0.3080184517881603,"score_spread":0.272992241859485,"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."}}