{"id":"W2961427308","doi":"10.5194/ica-proc-2-128-2019","title":"Semantically Enriched Simplification of Trajectories","year":2019,"lang":"en","type":"article","venue":"Proceedings of the ICA","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; University of New Brunswick","funders":"","keywords":"Trajectory; Computer science; Offset (computer science); Algorithm; Raster data; Process (computing); Dimension (graph theory); Data structure; Path (computing); Tracing; Object (grammar); Data point; Data mining; Artificial intelligence; Mathematics; Raster graphics","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":[],"consensus_categories":[],"category_scores_codex":[0.0001911305,0.00005437822,0.0001010239,0.00003722763,0.00002237801,0.00004317779,0.001172084,0.00001904402,0.000006817712],"category_scores_gemma":[0.0000541529,0.00003688206,0.00004286061,0.0003193974,0.00003535606,0.0004366875,0.0003278767,0.00004150986,0.00001182001],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005695407,"about_ca_system_score_gemma":0.000009409375,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004256327,"about_ca_topic_score_gemma":1.077853e-7,"domain_scores_codex":[0.9993767,0.000001958232,0.0001548785,0.0001467752,0.000224771,0.00009494395],"domain_scores_gemma":[0.9995023,0.00002233324,0.0001497896,0.0001866193,0.0001237177,0.00001523098],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000007356068,0.00007997369,0.01272333,0.0002073433,0.00002785422,3.775104e-8,0.0005772219,0.000002005524,0.1703271,0.8063932,0.00196527,0.007689318],"study_design_scores_gemma":[0.001267172,0.000429873,0.3417169,0.0002229301,0.00009234209,0.000005482556,0.0006692637,0.03704553,0.4807709,0.1179236,0.01919469,0.0006612528],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9563865,0.00003729919,0.009792073,0.003380329,0.000434513,0.0004675083,0.000003531934,0.00009735971,0.02940088],"genre_scores_gemma":[0.9908059,0.00000535628,0.00848591,0.0000373367,0.00001759968,0.000002611147,3.956115e-7,0.000003132144,0.0006417058],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6884696,"threshold_uncertainty_score":0.2178043,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006214568058820971,"score_gpt":0.1989891253084622,"score_spread":0.1927745572496412,"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."}}