{"id":"W7032970390","doi":"","title":"OPTIMAL METHODS OF COLLECTING COMMUNITY BENEFITS FROM DEVELOPERS WHEN HIGHER DENSITY IS GRANTED: A CASE STUDY IN THE CITY OF TORONTO","year":2013,"lang":"en","type":"dissertation","venue":"QSpace (Queen's University Library)","topic":"Educational Research and Methods","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Conformity; Urban planning; Regional planning; Transportation planning; Strategic planning; Data collection","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001652162,0.0002247885,0.0004969046,0.0001877487,0.0009225894,0.00006894983,0.001055434,0.0003010084,0.001617414],"category_scores_gemma":[0.0006321586,0.0002134037,0.0001479387,0.0006641464,0.0002124282,0.0009858846,0.0001929609,0.0007596347,0.000001507977],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003138051,"about_ca_system_score_gemma":0.001797574,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9490643,"about_ca_topic_score_gemma":0.3412972,"domain_scores_codex":[0.9912872,0.007182503,0.0002673397,0.0002998154,0.0006265363,0.0003366545],"domain_scores_gemma":[0.9952605,0.003379995,0.0003591427,0.0004449118,0.0003840585,0.0001713982],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.0004779514,0.001259967,0.1140251,0.0001228422,0.000373235,0.0001706795,0.8552396,0.000005174462,0.00002331995,0.001645173,0.02300305,0.003653893],"study_design_scores_gemma":[0.0003620645,0.0001239926,0.2142941,0.00009813477,0.000088789,2.921317e-7,0.7795894,9.834787e-7,0.0006022837,0.0002883881,0.004338311,0.0002132878],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9786799,0.00009623039,0.00003233669,0.002553139,0.0001928407,0.0009071009,0.0000703449,0.00002694693,0.01744119],"genre_scores_gemma":[0.8844983,0.0003123941,0.06097799,0.00006776032,0.00005899467,0.000006841748,0.0001251719,0.00002396709,0.05392859],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6077671,"threshold_uncertainty_score":0.9992952,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06884612889124397,"score_gpt":0.3713911408001137,"score_spread":0.3025450119088697,"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."}}