{"id":"W2506850060","doi":"10.3141/2541-02","title":"Demand-Sensitive Candidate Route Generation Algorithm","year":2016,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Public transport; Computer science; Transport engineering; Transit (satellite); Minification; Quality (philosophy); Service (business); Set (abstract data type); Level of service; Function (biology); Demand patterns; Path (computing); Operations research; Demand management; Engineering; Computer network; Economics","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":[0.001030696,0.0009716724,0.001577032,0.001024063,0.0007077705,0.001182941,0.002928214,0.001853983,0.007580497],"category_scores_gemma":[0.002629498,0.0005662719,0.0008379578,0.001269,0.0004859776,0.001174116,0.001311676,0.001034711,0.001354608],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009263394,"about_ca_system_score_gemma":0.001820576,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003449086,"about_ca_topic_score_gemma":0.003807635,"domain_scores_codex":[0.9992371,0.00020328,0.00004320744,0.0002145224,0.0001590355,0.0001428495],"domain_scores_gemma":[0.9986798,0.0006857988,0.0001130539,0.0001090419,0.0003403793,0.00007180122],"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":[0.0002324097,0.000147913,0.001137266,0.0001248687,0.00005028014,0.0001961615,0.00008781005,0.8654256,0.001825218,0.009664394,0.005744632,0.1153634],"study_design_scores_gemma":[0.00003454823,0.00003648411,0.00007640768,0.000004865314,0.000008505237,0.00004415252,0.00001803872,0.9957435,0.0003858196,0.0025904,0.001051794,0.000005637187],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02274237,0.000264138,0.9701033,0.0002852243,0.00006692565,0.0002517466,0.0003601992,0.0009292638,0.004996825],"genre_scores_gemma":[0.4369987,0.0002109763,0.5527242,0.0002217914,0.00005375855,0.0004397215,0.00167477,0.0001881924,0.007487855],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007580497,"threshold_uncertainty_score":0.02535927,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08440518504364633,"score_gpt":0.393056158829961,"score_spread":0.3086509737863147,"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."}}