{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003199524,0.0007713836,0.0006079476,0.002595543,0.0004737191,0.0005462874,0.001071417,0.000879975,0.001056512],"category_scores_gemma":[0.007735366,0.0004603127,0.001136108,0.001901793,0.0006750318,0.0017621,0.0006599372,0.001648978,0.0006545291],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008428094,"about_ca_system_score_gemma":0.001136654,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02833267,"about_ca_topic_score_gemma":0.03366245,"domain_scores_codex":[0.9991352,0.0002605081,0.00005741337,0.0002763029,0.0001739569,0.00009659395],"domain_scores_gemma":[0.9918543,0.005952561,0.0006869157,0.0007285347,0.0006545354,0.000123071],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005248493,0.0003036685,0.05263032,0.0001437547,0.0001205923,0.0002770282,0.000160509,0.759191,0.00355798,0.003725935,0.003835911,0.1755284],"study_design_scores_gemma":[0.000007406088,0.00002775206,0.006160858,0.00001302221,0.00001314401,0.00003461265,0.0000208718,0.9881889,0.000786896,0.004237004,0.0004909965,0.00001857821],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2073418,0.0004515486,0.7838514,0.0004834769,0.0001130336,0.0001510625,0.003401952,0.002854417,0.001351326],"genre_scores_gemma":[0.854284,0.0003343502,0.1364055,0.000113317,0.00009244607,0.0001467495,0.007197342,0.00007150945,0.001354607],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02833267,"threshold_uncertainty_score":0.05633551,"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."}}