{"id":"W576640945","doi":"","title":"What Do I Have and Where is it Located? Quantifying Ontario’s Municipal Lane Kilometres","year":2014,"lang":"fr","type":"article","venue":"Transportation 2014: Past, Present, Future - 2014 Conference and Exhibition of the Transportation Association of Canada // Transport 2014 : Du passé vers l'avenir - 2014 Congrès et Exposition de 'Association des transports du Canada","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Business; Kilometer; Asset (computer security); Context (archaeology); Asset management; Data collection; Transport engineering; Finance; Environmental resource management; Geography; Computer science; Economics; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001444342,0.0003228659,0.0003627132,0.003765761,0.002765398,0.003085081,0.001161283,0.0004882107,0.00620509],"category_scores_gemma":[0.009905309,0.0003131623,0.000431918,0.008861386,0.001233749,0.001212299,0.00192436,0.0003905839,0.001456402],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01584306,"about_ca_system_score_gemma":0.01908438,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9553292,"about_ca_topic_score_gemma":0.9837884,"domain_scores_codex":[0.9980959,0.0002486857,0.0001661797,0.0002543452,0.0007966218,0.0004382505],"domain_scores_gemma":[0.9917031,0.001009757,0.001584852,0.0003784476,0.004524069,0.0007997998],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008572039,0.00003032276,0.8641284,0.0006827572,0.0001138176,0.0002722108,0.02289621,0.0007829402,0.0005657816,0.004247331,0.05164795,0.05454674],"study_design_scores_gemma":[0.000008009263,0.00001781536,0.8740718,0.0004152977,0.0001027795,0.0001061963,0.03341361,0.001142558,0.0002516612,0.0006542628,0.0897643,0.00005174092],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7728175,0.003411191,0.005467064,0.005300955,0.0001328768,0.0004860982,0.06321311,0.0001810008,0.1489902],"genre_scores_gemma":[0.9581743,0.001877353,0.004754642,0.0002499195,0.00004557853,0.0003007076,0.01512006,0.00005850981,0.01941889],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04467082,"threshold_uncertainty_score":0.1149499,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0089019806074352,"score_gpt":0.2108945616517796,"score_spread":0.2019925810443444,"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."}}