{"id":"W2802097424","doi":"10.1111/fog.12272","title":"Environmental variability controls recruitment but with different drivers among spawning components in Gulf of St. Lawrence herring stocks","year":2018,"lang":"en","type":"article","venue":"Fisheries Oceanography","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"Fisheries and Oceans Canada","funders":"Government of Canada","keywords":"Herring; Zooplankton; Fishery; Ecology; Phenology; Dominance (genetics); Abundance (ecology); Biology; Predation; Stock (firearms); Fish stock; Environmental science; Geography; Fish <Actinopterygii>","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.0005986645,0.0001849788,0.0002052316,0.0005133934,0.0003094338,0.0004843331,0.0002208899,0.0001471283,0.0007378598],"category_scores_gemma":[0.001137647,0.0001623293,0.0003232833,0.0004672428,0.0002938905,0.0002308839,0.0003579757,0.0001485614,0.0001040908],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006157981,"about_ca_system_score_gemma":0.0005443292,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03944166,"about_ca_topic_score_gemma":0.1193944,"domain_scores_codex":[0.9997998,0.00004751048,0.00002240177,0.00005590852,0.00003590231,0.00003850635],"domain_scores_gemma":[0.9991401,0.0002170235,0.0003317567,0.00006267992,0.0001360507,0.0001124313],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005373515,0.00001422864,0.9919585,0.000009998132,0.00006695955,0.00004697392,0.00008858779,0.0003660992,0.004747279,0.00004601075,0.00005045202,0.002551082],"study_design_scores_gemma":[6.475334e-7,0.0000106544,0.999485,0.000001492971,0.000006580062,0.000007220936,0.00004361336,0.0003358265,0.00005983761,0.00001234918,0.00003524892,0.000001471663],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.999698,0.00003164717,0.00004633562,0.00001026452,6.62003e-7,8.21813e-7,0.00004855884,0.000002633069,0.0001610364],"genre_scores_gemma":[0.9997178,0.00001668188,0.00004754034,0.000007172776,0.000001225868,0.000001453666,0.00007779129,0.000001309022,0.0001291105],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9605584,"threshold_uncertainty_score":0.07842422,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02581565010602496,"score_gpt":0.223745076520065,"score_spread":0.19792942641404,"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."}}