{"id":"W4407086046","doi":"10.3390/ijgi14020058","title":"A Novel Context-Aware Douglas–Peucker (CADP) Trajectory Compression Method","year":2025,"lang":"en","type":"article","venue":"ISPRS International Journal of Geo-Information","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Trajectory; Computer science; Scalability; Context (archaeology); Piecewise; Robustness (evolution); Segmentation; Artificial intelligence; Algorithm; Mathematics","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":[0.0003156258,0.0009275958,0.0006096608,0.001010196,0.0005057385,0.0007592182,0.001374623,0.0009609267,0.002588782],"category_scores_gemma":[0.002058252,0.0004309876,0.0006981309,0.00141272,0.0004676344,0.001828524,0.001315112,0.00170349,0.0007977289],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006346451,"about_ca_system_score_gemma":0.001443159,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00763082,"about_ca_topic_score_gemma":0.006161822,"domain_scores_codex":[0.9996629,0.00004331259,0.00002676893,0.0001015842,0.0001193308,0.00004609528],"domain_scores_gemma":[0.9996092,0.0001094255,0.00003934429,0.00007561927,0.0001389557,0.00002751962],"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":[0.0002046756,0.00006408671,0.001021943,0.0001454168,0.00004338164,0.0002493472,0.0001771742,0.2115072,0.02217638,0.01388389,0.004898588,0.7456279],"study_design_scores_gemma":[0.00001487196,0.00005266934,0.0002965863,0.00001360277,0.00001337546,0.0001747433,0.0000379069,0.9842812,0.007599195,0.003240958,0.004256983,0.000017927],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006786976,0.0002544635,0.991394,0.0001031303,0.00006919725,0.00004534773,0.0001000102,0.0005611636,0.0006856747],"genre_scores_gemma":[0.2086055,0.000610073,0.7844963,0.0001954691,0.0001317388,0.0002098,0.0008519585,0.0002892885,0.004609962],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00763082,"threshold_uncertainty_score":0.01517284,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009724316283204451,"score_gpt":0.2934898225721461,"score_spread":0.2837655062889416,"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."}}