{"id":"W4323544062","doi":"10.25965/flamme.713","title":"#dérive : lire Montréal","year":2023,"lang":"fr","type":"article","venue":"Fédérer Langues Altérités Marginalités Médias Éthique","topic":"Cultural Insights and Digital Impacts","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Humanities; Art","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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":[0.0009436862,0.0009247992,0.0004019022,0.001503737,0.006479939,0.006316731,0.001341439,0.001421804,0.2812883],"category_scores_gemma":[0.003617575,0.0004599559,0.0003541077,0.001475874,0.002120998,0.003218614,0.003072964,0.001966736,0.04971952],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.009355647,"about_ca_system_score_gemma":0.005788522,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2983471,"about_ca_topic_score_gemma":0.5563712,"domain_scores_codex":[0.9988122,0.0002519572,0.00002746049,0.0002567052,0.0005178535,0.0001338355],"domain_scores_gemma":[0.9989001,0.000283268,0.00003603561,0.0001264834,0.0004003804,0.0002537574],"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":[0.0001506848,0.00006115383,0.002003773,0.0002664036,0.00001130481,0.0008140825,0.01521497,0.0002786725,0.004070729,0.1289286,0.6377078,0.2104919],"study_design_scores_gemma":[0.000003004696,0.000007520388,0.0004524872,0.00003436752,0.000001319733,0.00007287574,0.0008410456,0.00009805476,0.000393878,0.0007657149,0.9973188,0.0000110444],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.01080107,0.003588353,0.01056242,0.01628322,0.002880761,0.0002047545,0.002577291,0.003055018,0.9500472],"genre_scores_gemma":[0.03891913,0.001525096,0.005088622,0.00114556,0.0003077743,0.00009667393,0.0006366461,0.001226509,0.9510539],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7016529,"threshold_uncertainty_score":0.9410031,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2463955240665673,"score_gpt":0.3327941926582225,"score_spread":0.08639866859165526,"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."}}