{"id":"W3190627552","doi":"10.1016/j.pocean.2021.102659","title":"Disentangling diverse responses to climate change among global marine ecosystem models","year":2021,"lang":"en","type":"article","venue":"Progress In Oceanography","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":115,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University; Fisheries and Oceans Canada; Memorial University of Newfoundland; University of British Columbia; McGill University","funders":"European Commission; Ministerio de Ciencia, Innovación y Universidades; Agence Nationale de la Recherche; Open Philanthropy Project; Fisheries and Oceans Canada; Jarislowsky Foundation","keywords":"Ecosystem; Marine ecosystem; Environmental science; Biomass (ecology); Climate change; Ecosystem model; Trophic level; Ecosystem services; Environmental resource management; Ecology; Global warming; Biology","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":[0.002691211,0.0005880357,0.0004546573,0.0005788708,0.000443025,0.0009897787,0.000501746,0.0004602682,0.0006297799],"category_scores_gemma":[0.006218032,0.0003971249,0.001483038,0.0005019253,0.0005465109,0.001064251,0.001322071,0.0008677577,0.00008582148],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006273931,"about_ca_system_score_gemma":0.0004696151,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008329687,"about_ca_topic_score_gemma":0.0142409,"domain_scores_codex":[0.9993001,0.0004177392,0.00005107537,0.0001094331,0.0000672837,0.0000543227],"domain_scores_gemma":[0.9978871,0.001425711,0.0001836176,0.0002721041,0.0001271086,0.0001042508],"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":[0.0006991515,0.0003293779,0.3390152,0.000298761,0.002400356,0.0001775417,0.0007376808,0.6110559,0.01919711,0.006714767,0.0007686506,0.01860542],"study_design_scores_gemma":[0.00008208272,0.0003775432,0.1820884,0.00004223069,0.0004936691,0.00005369053,0.0005481254,0.802187,0.002036394,0.01015657,0.001858601,0.0000756872],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9943652,0.0001670073,0.003920524,0.0001444094,0.000009480314,0.00002096407,0.0002740379,0.0000470392,0.001051327],"genre_scores_gemma":[0.9975169,0.0001008272,0.00183809,0.0000508764,0.000004216915,0.0000301212,0.0003258632,0.00002352724,0.0001096025],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008329687,"threshold_uncertainty_score":0.0165624,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03130781515092245,"score_gpt":0.2834799795228771,"score_spread":0.2521721643719546,"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."}}