{"id":"W2384795723","doi":"","title":"Deriving Parking Use from Household Travel Survey Data","year":2016,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Smart Parking Systems Research","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Transport engineering; TRIPS architecture; Parking guidance and information; Duration (music); Population; Travel survey; Data collection; Sustainable transport; Land use; Travel behavior; Computer science; Scale (ratio); Usability; Geography; Business; Engineering; Sustainability; Civil engineering; Statistics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001768173,0.000478056,0.0005370129,0.0004471761,0.0002217294,0.0004424023,0.00174877,0.0003738505,0.00005604763],"category_scores_gemma":[0.001154222,0.000411681,0.0001023152,0.0005644805,0.0001084615,0.001169167,0.0006490739,0.0004700831,0.00006522435],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006212958,"about_ca_system_score_gemma":0.0001295042,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.04918052,"about_ca_topic_score_gemma":0.02616271,"domain_scores_codex":[0.9962807,0.0003742453,0.0006773635,0.0007938335,0.0006577217,0.001216162],"domain_scores_gemma":[0.9948123,0.001492487,0.0001145754,0.00306774,0.0000868523,0.0004260293],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006596158,0.00008958356,0.7835225,0.00005647553,0.0002636362,0.000139654,0.0001763381,0.002642918,0.1540251,0.0002394226,0.02179673,0.03698173],"study_design_scores_gemma":[0.0006054072,0.00002717992,0.8812645,0.0003159147,0.0000237196,0.00004310997,0.00003221131,0.09338548,0.01862807,0.0001110412,0.004825827,0.0007375478],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4672173,0.0009144394,0.527219,0.0002069667,0.0004218429,0.000510542,0.001508363,0.001801691,0.0001998738],"genre_scores_gemma":[0.9846237,0.0003273332,0.01346641,0.0001449464,0.0003407796,0.0001927206,0.0001902778,0.0002631255,0.0004507434],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5174063,"threshold_uncertainty_score":0.9998335,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06526558137833885,"score_gpt":0.2569810370195871,"score_spread":0.1917154556412482,"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."}}