{"id":"W7039644531","doi":"","title":"New preliminary data on VME encounters in NAFO Regulatory Area (Divs. 3LMNO) from EU; EU-Spain Groundfish Surveys (2019) and Canadian surveys (2018 and spring 2019)","year":2019,"lang":"en","type":"other","venue":"DIGITAL.CSIC (Spanish National Research Council (CSIC))","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Groundfish; Spring (device); Fishing","routes":{"ca_aff":false,"ca_fund":false,"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":[0.00141403,0.0004691686,0.0002925079,0.004224639,0.0006589812,0.0007302925,0.0006182274,0.0004292291,0.01042114],"category_scores_gemma":[0.002896747,0.0003022942,0.0003836032,0.003359327,0.000288724,0.0006544932,0.001328642,0.0005226312,0.002562999],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001835521,"about_ca_system_score_gemma":0.001975411,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1137988,"about_ca_topic_score_gemma":0.322612,"domain_scores_codex":[0.998577,0.0001465154,0.0001536041,0.0002455525,0.000688098,0.0001893425],"domain_scores_gemma":[0.9971268,0.0002838378,0.0005641295,0.0001982282,0.001595398,0.0002316431],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007140311,0.0002460571,0.6034347,0.0009736173,0.0001052691,0.0004136862,0.004638085,0.001266962,0.003305321,0.001914096,0.252887,0.1301011],"study_design_scores_gemma":[0.00002001849,0.00009949318,0.7900242,0.0001974826,0.00001593455,0.00007912146,0.003018097,0.000255016,0.0004353461,0.0001631622,0.2056663,0.00002591307],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.3529067,0.0009055548,0.003015266,0.00077276,0.0003011489,0.001094486,0.5566542,0.00030447,0.08404533],"genre_scores_gemma":[0.3005631,0.001147858,0.01385015,0.0004466788,0.00009920732,0.003083457,0.6273144,0.0001749852,0.05332018],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8862013,"threshold_uncertainty_score":0.2262728,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1590592564045483,"score_gpt":0.347279824272086,"score_spread":0.1882205678675377,"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."}}