{"id":"W2526469080","doi":"10.15439/2016f546","title":"Identifying Fishing Activities from AIS Data with Conditional Random Fields","year":2016,"lang":"en","type":"article","venue":"Annals of Computer Science and Information Systems","topic":"Maritime Navigation and Safety","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; National Research Centre","keywords":"Fishing; Conditional random field; Computer science; Artificial intelligence; Fishery","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004899643,0.00005595227,0.0001044594,0.00009929861,0.00008184786,0.0002177339,0.0002268231,0.00002507509,0.00001312422],"category_scores_gemma":[0.00001691521,0.00003715036,0.000008792621,0.0001301797,0.00009728329,0.008239567,0.00006553734,0.00003255938,0.000006459159],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005982163,"about_ca_system_score_gemma":0.00003276644,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003679383,"about_ca_topic_score_gemma":0.000002205233,"domain_scores_codex":[0.9992483,0.00001057106,0.0002287464,0.00007161609,0.000344581,0.00009620604],"domain_scores_gemma":[0.999433,0.0001010283,0.00007001006,0.0001847049,0.0001663202,0.00004498227],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001944725,0.00004018205,0.005162769,0.001021781,0.0003066927,0.000004006743,0.0129345,0.02804268,0.003002031,0.03749213,0.1055932,0.8062056],"study_design_scores_gemma":[0.001837486,0.00007252557,0.01632308,0.0007897494,0.000008243866,0.00002214271,0.0004629253,0.9103327,0.007040835,0.000469689,0.06229088,0.0003497507],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08355573,0.0000529832,0.9134023,0.0003413394,0.0002990165,0.0001007906,0.0001544276,0.00005822878,0.002035154],"genre_scores_gemma":[0.999099,0.0000537177,0.0005353916,0.0001959599,0.00005870323,0.000002995534,0.00004582301,0.000001655111,0.000006717072],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9155433,"threshold_uncertainty_score":0.5973487,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0410964016023319,"score_gpt":0.2671538348999595,"score_spread":0.2260574332976276,"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."}}