{"id":"W2793245693","doi":"","title":"Trading Density for Benefits: Section 37 Agreements in Toronto","year":2013,"lang":"en","type":"article","venue":"TSpace (University of Toronto)","topic":"Underground infrastructure and sustainability","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of Toronto; TD Bank","keywords":"CLARITY; Legislation; Discretion; Value (mathematics); Variety (cybernetics); Public economics; Business; Downtown; Political science; Economics; Geography; Law; Computer science","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00007793411,0.0001094036,0.0001778768,0.00001557873,0.00007936632,0.0000116991,0.0001271868,0.0001019412,0.006141936],"category_scores_gemma":[0.000008907418,0.0001418226,0.00007110094,0.00003215846,0.00002664691,0.001083557,0.00002776349,0.00005651498,0.000002135501],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001848179,"about_ca_system_score_gemma":0.00001553848,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.2591035,"about_ca_topic_score_gemma":0.3644102,"domain_scores_codex":[0.9994391,0.00001518158,0.00008305933,0.0001505837,0.000091831,0.0002203056],"domain_scores_gemma":[0.9996403,0.00002764853,0.00003178776,0.0001644703,0.00007320104,0.00006256487],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0005260882,0.0005202972,0.08491685,0.002269634,0.0008144174,0.00001531838,0.1135116,0.01249013,0.03310136,0.00982139,0.02983914,0.7121738],"study_design_scores_gemma":[0.001020795,0.0001257101,0.9235269,0.00002506716,0.00002913787,0.000001453535,0.04519805,0.02798666,0.0002436633,0.0005619096,0.001016576,0.0002640153],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9544296,0.000359205,0.007418319,0.00007865423,0.0002406428,0.0003956689,0.000004369252,0.00008300768,0.03699048],"genre_scores_gemma":[0.9972273,0.00008707754,0.002247929,0.000008250239,0.00004028621,0.00000113491,0.00000612127,0.000009538278,0.0003723453],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8386101,"threshold_uncertainty_score":0.9947666,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007086945305876502,"score_gpt":0.1999244578368162,"score_spread":0.1928375125309397,"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."}}