{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003160855,0.0001802283,0.0002062997,0.0001066183,0.0001366035,0.0001161633,0.0003354411,0.0000691661,0.001273661],"category_scores_gemma":[0.00002433,0.0001767604,0.0001104473,0.001570606,0.0001579985,0.000446656,0.001959688,0.0001386213,0.00005183535],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001230689,"about_ca_system_score_gemma":0.000008127179,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004679998,"about_ca_topic_score_gemma":0.001672647,"domain_scores_codex":[0.9979486,0.0001405514,0.0002548182,0.0005105091,0.0004956871,0.0006498492],"domain_scores_gemma":[0.9992813,0.00003077006,0.00004817189,0.0003809549,0.00002274106,0.0002360663],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001314147,0.0001207421,0.9533705,0.00003968868,0.00001014302,0.000341777,0.0002684675,0.00002751344,3.877243e-7,0.0002174622,0.000112735,0.04535919],"study_design_scores_gemma":[0.0004849207,0.00010388,0.9839525,0.00005549039,0.0000121242,0.00001367577,0.0005974526,0.008450267,0.00004757127,0.0007079284,0.005215403,0.0003588225],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9508966,0.00008280284,0.00002291518,0.0002766259,0.0001298503,0.0004370581,0.00006923993,0.00006909858,0.0480158],"genre_scores_gemma":[0.9978222,0.0002271481,0.00149474,0.0001420988,0.00005560727,0.0001449457,0.00002172511,0.0000167102,0.0000748228],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04794097,"threshold_uncertainty_score":0.9996393,"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."}}