{"id":"W2752356979","doi":"10.1139/cjfas-2016-0446","title":"On the precision of predicting fishing location using data from the vessel monitoring system (VMS)","year":2017,"lang":"en","type":"article","venue":"Canadian Journal of Fisheries and Aquatic Sciences","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Fishing; Computer science; Otter; Fishery; Misrepresentation","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.01502454,0.000991719,0.0008082888,0.002795035,0.0006570338,0.002941217,0.001149866,0.00137813,0.0006493633],"category_scores_gemma":[0.07748697,0.000532533,0.001108616,0.002522407,0.0009334747,0.003727293,0.002069994,0.001241713,0.0007078082],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009768988,"about_ca_system_score_gemma":0.001050843,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03896282,"about_ca_topic_score_gemma":0.03272618,"domain_scores_codex":[0.9926177,0.002816736,0.000668312,0.002372745,0.001132653,0.0003919298],"domain_scores_gemma":[0.932126,0.04654469,0.006978358,0.008188194,0.005675123,0.0004876553],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005712562,0.00008139479,0.8088726,0.0001530934,0.0007956118,0.00009425649,0.0005808395,0.1161425,0.001520468,0.001088895,0.001554424,0.06854451],"study_design_scores_gemma":[0.00005502102,0.0003350225,0.4733263,0.0003020658,0.0003820342,0.0002924287,0.001097562,0.5109568,0.004668368,0.004947769,0.00348506,0.0001515122],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9367651,0.001386072,0.05297455,0.0005656338,0.0001896257,0.00005658865,0.002734873,0.0005357474,0.004791742],"genre_scores_gemma":[0.987333,0.0002166473,0.009639474,0.00008348396,0.00004887568,0.00001064513,0.0022967,0.00004060234,0.0003307562],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03896282,"threshold_uncertainty_score":0.07945836,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09805385170944923,"score_gpt":0.2899589582299228,"score_spread":0.1919051065204735,"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."}}