{"id":"W4416975995","doi":"10.1093/icesjms/fsaf198","title":"The importance of engagement with fisheries, aquaculture, and Indigenous communities in the planning and implementation of marine carbon dioxide removal (mCDR)","year":2025,"lang":"en","type":"article","venue":"ICES Journal of Marine Science","topic":"Ocean Acidification Effects and Responses","field":"Earth and Planetary Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Fisheries and Oceans Canada","funders":"Korea Coast Guard; National Oceanic and Atmospheric Administration; Ocean Foundation","keywords":"Indigenous; Community engagement; Leverage (statistics); Fishing; Government (linguistics); Best practice; Scale (ratio); Climate change","routes":{"ca_aff":true,"ca_fund":false,"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":[],"consensus_categories":[],"category_scores_codex":[0.002628232,0.00005822641,0.0001102649,0.000108688,0.0002357605,0.0000629545,0.0003038438,0.00001024144,0.00001233859],"category_scores_gemma":[0.00005731775,0.00002676917,0.00001051421,0.0003437278,0.00053129,0.0001872976,0.00005433557,0.000125522,1.492114e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004154437,"about_ca_system_score_gemma":0.0001123521,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002722671,"about_ca_topic_score_gemma":0.007002462,"domain_scores_codex":[0.9991137,0.0001553166,0.0002758547,0.00005941197,0.0002849006,0.0001107564],"domain_scores_gemma":[0.9989764,0.0004849399,0.0003333566,0.0001038138,0.00007930033,0.00002215845],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001097171,0.000006649029,0.9597902,0.000025193,0.000009785168,0.000006997196,0.004209518,0.00009441581,0.0001423437,0.00007789891,0.000003997686,0.03552333],"study_design_scores_gemma":[0.0002331666,0.0002486596,0.9785354,0.00003907437,0.00001204826,0.00005977922,0.01924796,0.0001694085,0.0005819243,0.0002101902,0.0006297863,0.00003263545],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9968309,0.0009952134,0.000004562824,0.0003082657,0.00003857777,0.0001091739,0.000003323751,0.000001129142,0.001708858],"genre_scores_gemma":[0.9988822,0.0005558581,0.0004714916,0.00005469068,0.000007611153,2.763122e-7,0.000001875933,6.678837e-7,0.00002537678],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0354907,"threshold_uncertainty_score":0.4115883,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01519793060619066,"score_gpt":0.2657595467436498,"score_spread":0.2505616161374591,"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."}}