{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004082736,0.0001119488,0.0002122292,0.00007069792,0.0001348078,0.00002041876,0.000286387,0.00002318082,0.0001009937],"category_scores_gemma":[0.0001073523,0.00008487486,0.00006231254,0.0005540057,0.0002993732,0.0003142849,0.0008361348,0.0001573317,0.000004560563],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000424595,"about_ca_system_score_gemma":0.00001776615,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000360868,"about_ca_topic_score_gemma":0.00001604443,"domain_scores_codex":[0.9989226,0.000005722115,0.0002705753,0.0001392812,0.0004494621,0.0002123443],"domain_scores_gemma":[0.9995489,0.0000215991,0.0001308612,0.00007357873,0.00004993252,0.0001751173],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00009286885,0.00005489251,0.7227019,0.0001198044,0.00003137166,0.00001186736,0.002150426,0.005384165,0.2302337,0.00001408029,0.001833151,0.03737176],"study_design_scores_gemma":[0.0005160624,0.0004828238,0.9601361,0.0000455489,0.00003850349,0.0001119957,0.0001890185,0.006473759,0.01544995,0.000006075539,0.01633087,0.0002193277],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9977216,0.0000682829,0.00004586716,0.0007620468,0.00009538201,0.00004810115,9.550803e-7,0.000007133142,0.001250692],"genre_scores_gemma":[0.9977918,0.0002380687,0.001653406,0.0001825107,0.00009291617,7.553328e-7,2.068561e-7,0.0000052443,0.00003512744],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2374342,"threshold_uncertainty_score":0.3461097,"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."}}