{"id":"W2140364566","doi":"10.1577/t05-152.1","title":"Climate Regime Effects on Pacific Herring Growth Using Coupled Nutrient‐Phytoplankton‐Zooplankton and Bioenergetics Models","year":2008,"lang":"en","type":"article","venue":"Transactions of the American Fisheries Society","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"Fisheries and Oceans Canada","funders":"National Marine Fisheries Service; Heiwa Nakajima Foundation; International Science and Technology Center","keywords":"Herring; Zooplankton; Phytoplankton; Trophic level; Pacific herring; Environmental science; Bioenergetics; Clupea; Biomass (ecology); Predation; Upwelling; Nutrient; Ecology; Ecosystem model; Population; Oceanography; Ecosystem; Fishery; Biology; Fish <Actinopterygii>; Geology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0005312615,0.0007967429,0.0004703957,0.000363046,0.0005174426,0.001019544,0.0008049387,0.0007197648,0.001180362],"category_scores_gemma":[0.001330964,0.0005759035,0.0008930214,0.0003348246,0.0003827237,0.0005827531,0.0005217092,0.0004810826,0.0001088998],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002280977,"about_ca_system_score_gemma":0.001578336,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2340389,"about_ca_topic_score_gemma":0.1127605,"domain_scores_codex":[0.9998615,0.00004482266,0.000008311158,0.00003815404,0.00001855805,0.00002860534],"domain_scores_gemma":[0.9995434,0.000232654,0.00005315879,0.00002679705,0.00008485375,0.00005922233],"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":[0.00005036236,0.00002641051,0.01512603,0.00001131932,0.00006049395,0.00004138924,0.00002206445,0.9829276,0.0006553184,0.0001893436,0.00005775401,0.0008318291],"study_design_scores_gemma":[0.00003223184,0.00003525659,0.006335401,0.000003438472,0.00003205092,0.00000608957,0.00002093175,0.9931203,0.0002011898,0.0001150351,0.0000876285,0.00001047066],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9963529,0.00004111214,0.001896642,0.00005175866,0.000005837545,0.00001113446,0.0002181962,0.00005370098,0.001368711],"genre_scores_gemma":[0.9988558,0.00003395792,0.0006113268,0.00001304983,0.000002213006,0.00001126377,0.0001327999,0.000008716008,0.0003308094],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2340389,"threshold_uncertainty_score":0.4653533,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01905323745477213,"score_gpt":0.2226304433985153,"score_spread":0.2035772059437432,"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."}}