{"id":"W2095995927","doi":"10.1177/1473871612456121","title":"Interactive exploration of movement data: A case study of geovisual analytics for fishing vessel analysis","year":2012,"lang":"en","type":"article","venue":"Information Visualization","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Computer science; Analytics; Filter (signal processing); Data mining; Movement (music); Range (aeronautics); Process (computing); Focus (optics); Dimension (graph theory); Path (computing); Visual analytics; Fractal dimension; Fractal; Computer vision; Visualization","routes":{"ca_aff":true,"ca_fund":false,"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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0009597425,0.0001311604,0.0002788419,0.0008274008,0.0001041964,0.0001620185,0.0004720254,0.00005078181,0.000009210778],"category_scores_gemma":[0.0003210462,0.0001316149,0.00006065042,0.002034487,0.00001804293,0.01794876,0.0002761373,0.00004120115,0.000002678126],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005050373,"about_ca_system_score_gemma":0.00005087875,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001892469,"about_ca_topic_score_gemma":0.00006477907,"domain_scores_codex":[0.9981037,0.00009948666,0.001019816,0.0001601095,0.0004511104,0.0001658209],"domain_scores_gemma":[0.9972867,0.0001338031,0.001057821,0.0006738544,0.0007780194,0.00006975163],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001763614,0.00891312,0.07340498,0.001089493,0.004642114,0.000007755199,0.3731383,0.1798001,0.0003088402,0.3117949,0.007104808,0.03961925],"study_design_scores_gemma":[0.0006697039,0.0001932441,0.0007674715,0.00001325165,0.0003537823,0.000003265991,0.01892257,0.9773654,0.0007775965,0.0000871947,0.0007068552,0.0001396887],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0779313,0.000006259357,0.9211809,0.00002462903,0.0001516355,0.0004844655,0.0001201677,0.00004455643,0.00005609035],"genre_scores_gemma":[0.9940991,0.000008656104,0.003825283,0.0001335788,0.00003507385,0.00002258978,0.001860531,0.000005943501,0.000009211522],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9173556,"threshold_uncertainty_score":0.9957867,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09741630888681105,"score_gpt":0.3953757689870471,"score_spread":0.297959460100236,"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."}}