{"id":"W2084718945","doi":"10.1371/journal.pbio.1001303","title":"Using Grizzly Bears to Assess Harvest-Ecosystem Tradeoffs in Salmon Fisheries","year":2012,"lang":"en","type":"article","venue":"PLoS Biology","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":70,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; Raincoast Conservation Foundation","funders":"Natural Sciences and Engineering Research Council of Canada; Pacific Salmon Foundation; Alaska Department of Fish and Game; Massachusetts Department of Fish and Game; National Science Foundation","keywords":"Ecosystem; Fishery; Stock (firearms); Threatened species; Fish stock; Fisheries management; Biomass (ecology); Grizzly Bears; Productivity; Marine ecosystem; Stock assessment; Population; Ecology; Biology; Fish <Actinopterygii>; Geography; Fishing; Habitat; Ursus","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":[],"consensus_categories":[],"category_scores_codex":[0.001934466,0.0003561366,0.0002045467,0.0007767821,0.0005010862,0.0006542456,0.0003016148,0.0003447416,0.0006784875],"category_scores_gemma":[0.00193284,0.0001541521,0.000371292,0.0004090692,0.0003993174,0.000520464,0.0005151284,0.0003752708,0.0001421808],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005098396,"about_ca_system_score_gemma":0.0002534906,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01130007,"about_ca_topic_score_gemma":0.03738383,"domain_scores_codex":[0.9996154,0.0001524311,0.00002305361,0.00009831898,0.00007276469,0.00003799514],"domain_scores_gemma":[0.9989436,0.0002213697,0.0004955678,0.00009597679,0.00008700568,0.0001563509],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000198337,0.0001055584,0.9922552,0.000008444944,0.0000951086,0.00003143674,0.0001618614,0.000742854,0.002139327,0.000205014,0.00008413036,0.003972718],"study_design_scores_gemma":[0.000007071957,0.0003340945,0.995466,0.000005398059,0.00002836404,0.00006683942,0.0003252342,0.002521319,0.0007456783,0.0002742505,0.0002143533,0.00001137328],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9981962,0.00003148743,0.0006100438,0.00002372694,0.000003030628,0.00001930384,0.0001356104,0.000007470366,0.0009733053],"genre_scores_gemma":[0.9976571,0.00002830293,0.001756935,0.00003831311,0.000004864572,0.00003452192,0.0002041102,0.000002573232,0.000273258],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01130007,"threshold_uncertainty_score":0.02246857,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07736703975673286,"score_gpt":0.2730767231791856,"score_spread":0.1957096834224528,"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."}}