{"id":"W2136983157","doi":"10.1093/icesjms/fsq117","title":"Will depleted populations of Pacific salmon recover under persistent reductions in survival and catastrophic mortality events?","year":2010,"lang":"en","type":"article","venue":"ICES Journal of Marine Science","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Fisheries and Oceans Canada","funders":"Fisheries and Oceans Canada; Princeton University","keywords":"Fishing; Benchmark (surveying); Environmental science; Maximum sustainable yield; Fishery; Abundance (ecology); Fish <Actinopterygii>; Ecology; Biology; Geography; Fisheries management","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":[0.003619059,0.0002894132,0.0004522732,0.0004156142,0.0005252262,0.001119191,0.0008201829,0.0006216011,0.002231291],"category_scores_gemma":[0.009146235,0.0001930522,0.0005510038,0.0004244395,0.0009354735,0.001098722,0.0007621614,0.0006424246,0.0002233103],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001983841,"about_ca_system_score_gemma":0.001814693,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03230855,"about_ca_topic_score_gemma":0.03318824,"domain_scores_codex":[0.9992784,0.0002660461,0.00004201775,0.00007367049,0.0001752036,0.0001645959],"domain_scores_gemma":[0.9973649,0.001185095,0.0005577749,0.0001355217,0.0005368176,0.000219955],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.005418282,0.0006466348,0.3829899,0.0006223111,0.0009859167,0.001217264,0.0006576264,0.4606606,0.007073279,0.01235837,0.01501753,0.1123523],"study_design_scores_gemma":[0.0005612231,0.008974621,0.4515492,0.0006556778,0.0009081424,0.0009599669,0.005830538,0.4337107,0.01105777,0.07354439,0.01195122,0.000296532],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9754665,0.001393696,0.007136235,0.005743565,0.00007574375,0.0001167175,0.001063154,0.0000671514,0.008937256],"genre_scores_gemma":[0.998256,0.0003358471,0.0005686893,0.0001249846,0.000008195339,0.00001863649,0.0001570347,0.000003397905,0.0005271577],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03230855,"threshold_uncertainty_score":0.06424099,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02346369503094995,"score_gpt":0.2658317079902043,"score_spread":0.2423680129592543,"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."}}