{"id":"W2583491309","doi":"","title":"Determining deep-sea coral distributions in the northern Gulf of St. Lawrence using bycatch records and local ecological knowledge (LEK)","year":2016,"lang":"en","type":"dissertation","venue":"Memorial University Research Repository (Memorial University)","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Fishery; Bycatch; Halibut; Coral; Groundfish; Oceanography; Geography; Fishing; Commercial fishing; Biology; Fisheries management; Fish <Actinopterygii>; Geology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003208166,0.0001704801,0.0001314766,0.001558857,0.0003834486,0.0004391858,0.0002194778,0.0001192873,0.000518754],"category_scores_gemma":[0.0005961492,0.0001493457,0.0001527701,0.001244021,0.0002280692,0.0002430597,0.0004899783,0.0001156973,0.0001472251],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00130022,"about_ca_system_score_gemma":0.001247887,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.5497667,"about_ca_topic_score_gemma":0.853612,"domain_scores_codex":[0.99985,0.00001674931,0.00001903861,0.00003315531,0.00004282106,0.00003820552],"domain_scores_gemma":[0.9994304,0.00004437491,0.0002352197,0.00002955014,0.0001921476,0.00006829706],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001256761,0.000009659915,0.9947456,0.00001196214,0.00001143455,0.00002735446,0.0004136382,0.00005032027,0.0007528131,0.000008187926,0.0000576388,0.003898794],"study_design_scores_gemma":[8.459388e-7,0.00001453659,0.9986618,0.000007496271,0.00000536425,0.00001886292,0.0008075766,0.0001116323,0.00009585861,0.000003217278,0.0002711645,0.000001660804],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.999135,0.00003384741,0.00004197872,0.000007394853,4.909705e-7,0.000007198793,0.000353928,0.000002980747,0.0004172687],"genre_scores_gemma":[0.9975595,0.0001254867,0.0004840592,0.00001616526,0.000001134927,0.00001472538,0.001136743,0.0000014684,0.0006606254],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4502333,"threshold_uncertainty_score":0.9057696,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03083550000064256,"score_gpt":0.2738992567309482,"score_spread":0.2430637567303057,"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."}}