{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.001266232,0.0003394672,0.0005264545,0.0004945254,0.001358346,0.0001027575,0.001745265,0.0005908103,0.0008361554],"category_scores_gemma":[0.0002836713,0.0003033191,0.0001870514,0.001667163,0.001710276,0.000571791,0.001171308,0.001238002,0.00003820038],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001864223,"about_ca_system_score_gemma":0.0006774823,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.008707773,"about_ca_topic_score_gemma":0.02541207,"domain_scores_codex":[0.9950451,0.001779425,0.000348353,0.0008800391,0.001109304,0.0008377281],"domain_scores_gemma":[0.9977609,0.0008381255,0.0002337198,0.0005780931,0.0002955008,0.0002936698],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.03002239,0.004938122,0.7007945,0.001154886,0.000747613,0.01761442,0.01488589,0.0003101311,0.02369071,0.005511594,0.008560012,0.1917697],"study_design_scores_gemma":[0.01637409,0.005414866,0.1247817,0.0007830136,0.0007883331,0.0002181233,0.07211614,0.005894654,0.005152689,0.001224319,0.7633228,0.003929258],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.775083,0.00001077382,0.0002250663,0.00004062697,0.001352345,0.0006901009,0.00009335961,0.00002623022,0.2224785],"genre_scores_gemma":[0.9556013,0.0001514885,0.0001230672,0.000002032913,0.001023787,0.000001753799,0.0002053395,0.00003144108,0.04285981],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7547628,"threshold_uncertainty_score":0.9999419,"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."}}