{"id":"W7098055168","doi":"","title":"CANADA’S COMMERCIAL REAL ESTATE MARKETS PRIMED FOR GROWTH Highlights","year":2012,"lang":"en","type":"article","venue":"","topic":"Canadian Policy and Governance","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Real estate; Cash; Database transaction; Property management; Supply and demand; Corporate Real Estate; Collateral","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":[0.0007127975,0.0004178518,0.0003098209,0.001223668,0.01225812,0.01333326,0.001074122,0.002973283,0.05820289],"category_scores_gemma":[0.002780021,0.000352141,0.0004445846,0.001942271,0.002609218,0.002447762,0.00233613,0.004064853,0.003066857],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.06858393,"about_ca_system_score_gemma":0.1746644,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9835762,"about_ca_topic_score_gemma":0.9945787,"domain_scores_codex":[0.9982582,0.00005618657,0.00001806847,0.0001004412,0.000663755,0.0009033357],"domain_scores_gemma":[0.9950994,0.0002632526,0.0001418214,0.0001222474,0.001899474,0.00247385],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002569593,0.00002924136,0.006382768,0.0000674463,0.000008167375,0.0004590313,0.001230972,0.0001667054,0.0003232252,0.08300879,0.8881196,0.02017826],"study_design_scores_gemma":[0.00001545623,0.000009699907,0.02032041,0.0001034113,0.000007451696,0.0001003737,0.004638831,0.0003734505,0.0002199671,0.004651507,0.9695245,0.00003492688],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.05248095,0.00460946,0.0006071225,0.3836135,0.001776128,0.00008509393,0.005803509,0.0002913677,0.550733],"genre_scores_gemma":[0.4526374,0.00613258,0.001618347,0.04269996,0.0006675057,0.00004992956,0.002695644,0.0001919851,0.4933065],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.06858393,"threshold_uncertainty_score":0.4976135,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01921037323805537,"score_gpt":0.2780685141760688,"score_spread":0.2588581409380134,"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."}}