{"id":"W4411506031","doi":"10.1016/j.trc.2025.105173","title":"The flexible park-and-loop routing problem","year":2025,"lang":"en","type":"article","venue":"Transportation Research Part C Emerging Technologies","topic":"Smart Parking Systems Research","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada","keywords":"Loop (graph theory); Routing (electronic design automation); Computer science; Computer network; Transport engineering; Engineering; Operations research; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0007395321,0.0007846357,0.0008124595,0.000409776,0.0006215291,0.001126541,0.001217145,0.001464074,0.004154576],"category_scores_gemma":[0.001422363,0.0003666886,0.0007536229,0.0008447132,0.0007532201,0.001460683,0.0009998569,0.0008921467,0.0003618837],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006007786,"about_ca_system_score_gemma":0.000875338,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002146865,"about_ca_topic_score_gemma":0.002447981,"domain_scores_codex":[0.9994051,0.0002175926,0.00002546467,0.0001513413,0.00008431313,0.0001161036],"domain_scores_gemma":[0.9994444,0.0003230691,0.00007452419,0.00005439312,0.00003848357,0.00006503036],"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":[0.00009234087,0.00006485787,0.0004596542,0.0001039425,0.0000342649,0.0002706476,0.00005994855,0.9245192,0.001027895,0.04419501,0.002654869,0.02651739],"study_design_scores_gemma":[0.00004955882,0.0001142171,0.0002599087,0.00001460025,0.0000173584,0.0002303762,0.0000952374,0.9391268,0.0006567985,0.05308433,0.006333603,0.00001721507],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1348265,0.0006811352,0.8405654,0.001248306,0.0001777313,0.0002580468,0.001172255,0.0003123213,0.02075831],"genre_scores_gemma":[0.8489863,0.0004675506,0.1401973,0.0001549911,0.00007096725,0.0002068195,0.0008985319,0.0001055132,0.008912101],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004154576,"threshold_uncertainty_score":0.01389837,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04057208938099621,"score_gpt":0.3462026773020471,"score_spread":0.3056305879210509,"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."}}