{"id":"W3030865503","doi":"10.3390/jmse8060382","title":"Commercial Performance of Blue Mussel (Mytilus edulis, L.) Stocks at a Microgeographic Scale","year":2020,"lang":"en","type":"article","venue":"Journal of Marine Science and Engineering","topic":"Marine Bivalve and Aquaculture Studies","field":"Environmental Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministère de l'Agriculture, des Pêcheries et de l'Alimentation; Université du Québec à Rimouski","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Université du Québec à Rimouski; Ministère de l'Agriculture, des Pêcheries et de l'Alimentation","keywords":"Mussel; Mytilus; Fishery; Aquaculture; Blue mussel; Submarine pipeline; Productivity; Stock (firearms); Environmental science; Oceanography; Biology; Geography; Geology; Fish <Actinopterygii>","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0001901763,0.0002059475,0.0001101559,0.000459351,0.0003772164,0.000400328,0.0002171486,0.0001818984,0.0005866399],"category_scores_gemma":[0.0003398798,0.0001071019,0.000165598,0.0002710954,0.0002637787,0.0002766103,0.0003765008,0.0001314922,0.0001459117],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005999917,"about_ca_system_score_gemma":0.000305026,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02446704,"about_ca_topic_score_gemma":0.07585479,"domain_scores_codex":[0.9998729,0.00001254645,0.00001051665,0.00004677995,0.00003147137,0.00002579844],"domain_scores_gemma":[0.9995815,0.0000547683,0.0001379446,0.00003693794,0.00009463322,0.00009417739],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005318155,0.0002839986,0.7017008,0.00002977916,0.00006907072,0.000214008,0.0007106838,0.0004369311,0.284771,0.00004213001,0.00006984133,0.01113988],"study_design_scores_gemma":[0.000001970649,0.0003756681,0.9947023,0.000001078009,0.000008187906,0.00003510969,0.00015575,0.0001564374,0.004483553,0.00000492227,0.00007176045,0.000003251022],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9998614,0.000009260745,0.00001822616,0.000001218471,2.357568e-7,0.000001260715,0.0000292229,6.347254e-7,0.00007855862],"genre_scores_gemma":[0.9991935,0.00001751666,0.00009932998,0.000005108202,6.594691e-7,0.000004981322,0.0001637826,0.000001371375,0.0005137519],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02446704,"threshold_uncertainty_score":0.04864925,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005847267107512012,"score_gpt":0.1894969542525066,"score_spread":0.1836496871449946,"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."}}