{"id":"W7097700032","doi":"","title":"Bottom-Up Regulation of Capelin, a Keystone Forage Species","year":2013,"lang":"en","type":"article","venue":"","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Capelin; Ecosystem; Forage fish; Food web; Trophic level; Biomass (ecology); Productivity; Climate change; Ecosystem-based management","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008489122,0.0001838059,0.0001111975,0.0002965161,0.0003416935,0.0003976469,0.0002054509,0.0001326565,0.0009254105],"category_scores_gemma":[0.0004045752,0.0000968946,0.0000882466,0.0001712212,0.0003890546,0.00018313,0.0003611183,0.0001528565,0.0001315037],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001128196,"about_ca_system_score_gemma":0.0003907476,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05749593,"about_ca_topic_score_gemma":0.1434253,"domain_scores_codex":[0.9999305,0.000008626414,0.000003748042,0.00002639141,0.00001347549,0.0000173392],"domain_scores_gemma":[0.9997335,0.00002756815,0.0001177621,0.00001938987,0.00005153784,0.0000502827],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003408344,0.00008631527,0.5301517,0.0001209323,0.00006562964,0.0005339264,0.001322427,0.0004986994,0.4345659,0.0007232889,0.001369608,0.0302207],"study_design_scores_gemma":[0.000003054688,0.00004865471,0.9960409,0.00000789026,0.00001040983,0.00007476311,0.0001736935,0.0004546715,0.002286156,0.00006482263,0.0008294429,0.000005517411],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9978981,0.0002239783,0.0001680554,0.0000610827,0.000006958876,0.000005211784,0.0001121765,0.000007545279,0.001516796],"genre_scores_gemma":[0.998476,0.0001332562,0.0002477323,0.00006630345,0.000003444529,0.000007143707,0.00008582375,0.000004161057,0.0009760868],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05749593,"threshold_uncertainty_score":0.1143225,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01447084915051997,"score_gpt":0.225356498104184,"score_spread":0.210885648953664,"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."}}