{"id":"W4413193847","doi":"10.2139/ssrn.5377456","title":"<p>Locating Charging Stations Identifying Zones for Early Deployment in the GTHA using Real-World Truck Data</p>","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Electric Vehicles and Infrastructure","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University; Pembina Institute","funders":"","keywords":"Truck; Software deployment; Computer science; Environmental science; Transport engineering; Engineering; Automotive engineering; Operating system","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":[0.0004876221,0.0003672172,0.0002557376,0.001141274,0.0003850108,0.001429969,0.0005005868,0.0008589873,0.009008706],"category_scores_gemma":[0.002132883,0.000254209,0.0002220873,0.0009415788,0.000251418,0.0009917855,0.0005107725,0.000414466,0.003956792],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004963183,"about_ca_system_score_gemma":0.001044995,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04192938,"about_ca_topic_score_gemma":0.0577368,"domain_scores_codex":[0.9998434,0.00003343859,0.000005959269,0.0000291633,0.0000547416,0.00003338341],"domain_scores_gemma":[0.9994886,0.0001626485,0.00005573606,0.00007967301,0.0001529981,0.0000603447],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0009312437,0.0001991142,0.06758801,0.0002887146,0.0001008224,0.000797444,0.0005744004,0.1518061,0.04621232,0.01825845,0.1247252,0.5885182],"study_design_scores_gemma":[0.00007333265,0.0001978024,0.0456692,0.00006891126,0.00003886456,0.0001534357,0.001146329,0.8774486,0.02434647,0.0106816,0.04011022,0.00006535273],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4164125,0.0006072848,0.4462829,0.005354925,0.0008484477,0.0006089777,0.01984941,0.01128998,0.09874536],"genre_scores_gemma":[0.8783811,0.0002607935,0.09582818,0.0001511717,0.0000668128,0.00005750178,0.004940756,0.0004848704,0.01982885],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9580706,"threshold_uncertainty_score":0.08337069,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02774240971627381,"score_gpt":0.2929206121800489,"score_spread":0.2651782024637752,"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."}}