{"id":"W4415989979","doi":"10.5194/bg-22-6583-2025","title":"Marine heatwaves deeply alter marine food web structure and function","year":2025,"lang":"en","type":"article","venue":"Biogeosciences","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Fisheries and Oceans Canada","funders":"Social Sciences and Humanities Research Council; Région Bretagne; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Biomass (ecology); Trophic level; Food web; Ecosystem; Marine ecosystem; Primary producers; Latitude; Primary production; Ecosystem model","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":[0.0002040293,0.0003943795,0.0001713668,0.0001934344,0.000258817,0.000704546,0.0002477213,0.0005440662,0.001379137],"category_scores_gemma":[0.000610732,0.0002434814,0.0007781368,0.00019721,0.0002576903,0.0005200157,0.0003794015,0.0003636981,0.0001173344],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007779911,"about_ca_system_score_gemma":0.000407811,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02702311,"about_ca_topic_score_gemma":0.0189296,"domain_scores_codex":[0.9999297,0.00001922094,0.000005405526,0.00001630428,0.00000823534,0.0000212334],"domain_scores_gemma":[0.9998869,0.00003876902,0.00002453428,0.00001073083,0.00001645547,0.00002264446],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.00015631,0.0001758594,0.2355391,0.00005725153,0.0004278393,0.0002386727,0.00008325806,0.7431218,0.01288663,0.0009741646,0.0009398002,0.00539923],"study_design_scores_gemma":[0.0000468049,0.0001286365,0.1896686,0.00001648646,0.00008464046,0.0000511324,0.000146199,0.8070276,0.001406427,0.0007345858,0.0006626144,0.00002621384],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.996637,0.00004574556,0.001471974,0.00008657667,0.00002017047,0.000007273163,0.0003719153,0.00004563632,0.001313699],"genre_scores_gemma":[0.9991902,0.0000447777,0.0003310382,0.00002514675,0.000002679768,0.000004707977,0.0001661215,0.000008409719,0.0002270093],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02702311,"threshold_uncertainty_score":0.05373162,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008662441734434253,"score_gpt":0.225236764694541,"score_spread":0.2165743229601068,"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."}}