{"id":"W4386340261","doi":"10.1515/9781772124965-006","title":"2 BOOSTING EDMONTON Hockey and the Promotion of the City, 1894–1977","year":2019,"lang":"en","type":"book-chapter","venue":"University of Alberta Press eBooks","topic":"Sport and Mega-Event Impacts","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Boosting (machine learning); Promotion (chess); Computer science; Political science; Artificial intelligence; Law","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":[0.0002232931,0.0007763722,0.0001583009,0.001968414,0.003929009,0.002681413,0.0006614305,0.00122316,0.04407159],"category_scores_gemma":[0.0003239939,0.0002813339,0.0001924995,0.002133406,0.002030477,0.0008451246,0.001398865,0.001700717,0.004246468],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01244555,"about_ca_system_score_gemma":0.01168339,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.6846823,"about_ca_topic_score_gemma":0.9287338,"domain_scores_codex":[0.9997448,0.00001932989,0.000003973569,0.00003978173,0.0000752417,0.0001168567],"domain_scores_gemma":[0.9999187,0.00001011228,0.000006524249,0.000004068243,0.00003458286,0.00002604571],"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":[0.00008047915,0.00006031083,0.00229307,0.0003051783,0.000009559045,0.0004018849,0.01203901,0.0005196023,0.0002861253,0.3279438,0.4790613,0.1769997],"study_design_scores_gemma":[0.000001501256,0.000003736424,0.002633603,0.00007536603,0.000001749756,0.00002683504,0.0007698652,0.0000142127,0.00005128393,0.0006653332,0.9957535,0.000003086193],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.007289039,0.03053107,0.00033929,0.004765991,0.002078214,0.00004263899,0.0004694413,0.00003655285,0.9544477],"genre_scores_gemma":[0.03257789,0.007658799,0.0002573512,0.0006324692,0.0002026552,0.00001719962,0.0001536692,0.00002758784,0.9584723],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.3153177,"threshold_uncertainty_score":0.6343494,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03470599662108909,"score_gpt":0.2261107459706736,"score_spread":0.1914047493495845,"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."}}