{"id":"W2995893091","doi":"10.36939/cjur/vol28no2/art232","title":"Climate Change, Urban Responses and Sociospatial Transformations: The Example of Quebec City","year":2020,"lang":"en","type":"article","venue":"Canadian journal of urban research","topic":"French Urban and Social Studies","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Climate change; Corporate governance; Context (archaeology); Environmental planning; Institutionalisation; Political science; Adaptation (eye); Urban planning; Climate change adaptation; Geography; Environmental resource management; Business; Civil engineering; Environmental science; Ecology; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003429742,0.0003050751,0.0002402451,0.001194971,0.008802434,0.002580369,0.001156716,0.001106055,0.008539412],"category_scores_gemma":[0.0007903964,0.000111097,0.0003336539,0.003723666,0.002712246,0.0009757799,0.001595355,0.0009790102,0.0003682416],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05409991,"about_ca_system_score_gemma":0.01956133,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9962142,"about_ca_topic_score_gemma":0.9983786,"domain_scores_codex":[0.9995204,0.0001424591,0.000006097973,0.00003424735,0.00007563405,0.0002211341],"domain_scores_gemma":[0.9993013,0.0001486593,0.00004902825,0.00003095578,0.0002981269,0.0001718514],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"qualitative","study_design_scores_codex":[0.0005629759,0.0008495917,0.2239813,0.00105901,0.0002959649,0.01700025,0.1441539,0.01617982,0.00388087,0.2201875,0.2174745,0.1543744],"study_design_scores_gemma":[0.00007284169,0.0001132004,0.353081,0.0004625806,0.0001229378,0.0006311716,0.1836769,0.007678583,0.0006019099,0.004576232,0.4488088,0.0001739567],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7605728,0.004736144,0.001171241,0.02078196,0.000186454,0.0001277455,0.003192944,0.00009192491,0.2091389],"genre_scores_gemma":[0.9767514,0.001337617,0.0004773981,0.0009248687,0.00002150837,0.00002600164,0.0004353758,0.00001611017,0.02000959],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05409991,"threshold_uncertainty_score":0.3925242,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2849289129618682,"score_gpt":0.3620454408041675,"score_spread":0.07711652784229928,"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."}}