{"id":"W7018287229","doi":"","title":"Densification & affordability : comparative real estate projects across Montreal","year":2014,"lang":"en","type":"other","venue":"Open MIND","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Real estate; Context (archaeology); Urban planning; Real estate development; Process (computing); Government (linguistics); Residential real estate","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":[0.0006420935,0.0004205291,0.0003118876,0.002592944,0.00217364,0.002472596,0.001157133,0.0003042482,0.009271727],"category_scores_gemma":[0.002349738,0.0003430321,0.0003579019,0.007571201,0.001593737,0.001054658,0.001496486,0.0004214865,0.0003459383],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04040023,"about_ca_system_score_gemma":0.01518978,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9806123,"about_ca_topic_score_gemma":0.9938465,"domain_scores_codex":[0.9990736,0.0001811868,0.00002772214,0.0001317509,0.0003524733,0.0002331767],"domain_scores_gemma":[0.9985134,0.0001879368,0.0003028439,0.0001240691,0.0005889664,0.0002827563],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005415653,0.0003109844,0.8063012,0.0006159542,0.0004442979,0.001229191,0.02314392,0.01276945,0.003202192,0.03542461,0.01031181,0.1057048],"study_design_scores_gemma":[0.00001484781,0.00007208581,0.9624637,0.00007202025,0.00004115375,0.00006290158,0.01438279,0.001802584,0.000251384,0.0003252839,0.02047619,0.0000349264],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9759007,0.00111026,0.0006441545,0.0003003323,0.000008746993,0.000109637,0.003176164,0.00001997922,0.01873007],"genre_scores_gemma":[0.9905543,0.0004211808,0.000468176,0.00001869685,0.000002528557,0.00003991474,0.0009448387,0.000009080265,0.00754139],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04040023,"threshold_uncertainty_score":0.2931256,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04729753491077685,"score_gpt":0.3734104646983946,"score_spread":0.3261129297876177,"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."}}