{"id":"W6995815767","doi":"","title":"Planning for Intensifying Suburbs: Analyzing Markham and Vaughan","year":2019,"lang":"en","type":"other","venue":"York University Digital Library (York University)","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Metropolitan area; Downtown; Context (archaeology); Government (linguistics); Population; Urban planning; Order (exchange); Smart growth","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":[0.0009774477,0.0001596179,0.0001312985,0.0008935065,0.005382803,0.003071978,0.0012266,0.0006137745,0.005175429],"category_scores_gemma":[0.002508844,0.0001721544,0.0002213748,0.002009967,0.002929926,0.001679856,0.002414262,0.0009392519,0.0001973601],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02077354,"about_ca_system_score_gemma":0.01533816,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.6323011,"about_ca_topic_score_gemma":0.8966995,"domain_scores_codex":[0.9989771,0.0003530887,0.00001684893,0.00009492425,0.0002386616,0.0003195156],"domain_scores_gemma":[0.9984453,0.0004926213,0.0002004376,0.00005980895,0.0004108193,0.0003909082],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.00004095297,0.000151666,0.07973131,0.0003266945,0.00002428793,0.002314879,0.4674373,0.002513476,0.000393088,0.3475444,0.02606132,0.07346065],"study_design_scores_gemma":[0.000003647862,0.00003567849,0.08216918,0.0001937991,0.00001572477,0.0001310983,0.7054414,0.001150528,0.0002494606,0.007515871,0.2030691,0.00002450845],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7925175,0.001913204,0.001472018,0.01217597,0.00005868738,0.0001243901,0.000167405,0.00001919777,0.1915517],"genre_scores_gemma":[0.9791976,0.001179864,0.001278046,0.000378859,0.00001133851,0.00003466896,0.00006084424,0.000009913772,0.01784882],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3676989,"threshold_uncertainty_score":0.7397287,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01734166914504114,"score_gpt":0.1808808513601989,"score_spread":0.1635391822151578,"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."}}