{"id":"W2753982414","doi":"10.1145/3126973.3126979","title":"Compressing Trajectory for Trajectory Indexing","year":2017,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Research (Canada)","funders":"National Research Foundation","keywords":"Trajectory; Computer science; Global Positioning System; Raw data; Volume (thermodynamics); Search engine indexing; Data compression; Big data; Data mining; Artificial intelligence; Telecommunications","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004219989,0.001140798,0.00113463,0.003898658,0.001026046,0.001401273,0.001206214,0.0007703193,0.007262242],"category_scores_gemma":[0.004401511,0.0003381617,0.0009043068,0.008568937,0.0005610628,0.003045959,0.00204822,0.001258856,0.004879764],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006858076,"about_ca_system_score_gemma":0.001600432,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007113375,"about_ca_topic_score_gemma":0.005942694,"domain_scores_codex":[0.9990699,0.00007329732,0.0001061745,0.000233074,0.0004023951,0.0001152249],"domain_scores_gemma":[0.9985586,0.0002738111,0.0001158797,0.0005729086,0.0004320185,0.00004692202],"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":[0.0004698944,0.0001195567,0.002623647,0.0005658038,0.00007146086,0.0004336746,0.000369384,0.05455817,0.0189869,0.02776908,0.03032417,0.8637083],"study_design_scores_gemma":[0.00007139146,0.000283741,0.003811541,0.0001800672,0.00009578465,0.001818973,0.0008421979,0.7841866,0.04322471,0.07044972,0.09493188,0.0001033406],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0206338,0.001613333,0.9608886,0.0004129798,0.0004694329,0.00025286,0.007562908,0.00474711,0.003418945],"genre_scores_gemma":[0.2211273,0.004551849,0.7195396,0.0002055807,0.0005567922,0.0005162842,0.04496957,0.0007586495,0.007774374],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007262242,"threshold_uncertainty_score":0.02429456,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0538165772426006,"score_gpt":0.3001083062225393,"score_spread":0.2462917289799386,"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."}}