{"id":"W4384024051","doi":"10.1139/cjce-2022-0463","title":"Location–allocation strategies for traffic counters—a citywide deployment","year":2023,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Software deployment; Simulated annealing; Kriging; Transport engineering; Variance (accounting); Computer science; Traffic generation model; Operations research; Engineering; Real-time computing; Algorithm; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":true,"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":[0.0009362931,0.0007556183,0.0006859112,0.0007452175,0.0004752201,0.0007866293,0.001047519,0.0005801888,0.0009233503],"category_scores_gemma":[0.002156359,0.0004085705,0.0003671806,0.0008893624,0.0003686211,0.0009300776,0.0007496196,0.0004116199,0.0002061721],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001342875,"about_ca_system_score_gemma":0.001513001,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01101244,"about_ca_topic_score_gemma":0.01852226,"domain_scores_codex":[0.9992835,0.0003231632,0.00002301521,0.0001553341,0.0001111208,0.0001039469],"domain_scores_gemma":[0.9994004,0.0002172811,0.0001289603,0.00008041104,0.0001309662,0.0000421121],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0001038867,0.00007735155,0.004414459,0.00002910142,0.00003464582,0.00004559965,0.00006749284,0.9363495,0.005016101,0.002380864,0.0002910746,0.05119001],"study_design_scores_gemma":[0.000007451612,0.00007623213,0.001427767,0.000003730124,0.000018892,0.00002067117,0.00005978891,0.9956818,0.001604152,0.0005954415,0.0004899722,0.00001403396],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3048258,0.0001777257,0.6903445,0.0001811123,0.00002466906,0.0001621339,0.0001285582,0.0005171646,0.003638352],"genre_scores_gemma":[0.9234406,0.00005243481,0.0757477,0.00001518567,0.00000463153,0.00005506431,0.00007917474,0.00002003642,0.0005852685],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01101244,"threshold_uncertainty_score":0.02189666,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02111148330241224,"score_gpt":0.2551734788125725,"score_spread":0.2340619955101602,"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."}}