{"id":"W7048529374","doi":"","title":"Le repérage des zones
\\nde concentration de la
\\npauvreté à Montréal :
\\nl’identification des
\\nmicro, meso et macro
\\nzones de pauvreté","year":2008,"lang":"fr","type":"other","venue":"EspaceINRS (National Institute for Scientific Research (Canada))","topic":"Superconducting and THz Device Technology","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Population; Context (archaeology); Quantitative methodology; Statistical analysis","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":[0.002712047,0.0004964735,0.0005397296,0.002248424,0.002326399,0.004519447,0.001939637,0.0008803286,0.00793692],"category_scores_gemma":[0.01138102,0.000332774,0.0007074428,0.00473078,0.002366649,0.001559647,0.002807498,0.0009022304,0.0003254247],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02029984,"about_ca_system_score_gemma":0.02065255,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9227696,"about_ca_topic_score_gemma":0.9516279,"domain_scores_codex":[0.9973054,0.001174485,0.00007230978,0.0005525888,0.0004253582,0.0004698176],"domain_scores_gemma":[0.9947385,0.002606548,0.0007291712,0.0002972335,0.001265849,0.0003626708],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000557798,0.0001889274,0.4493244,0.001565344,0.00094945,0.0009728661,0.03063649,0.035258,0.003727034,0.2475246,0.02096429,0.2083309],"study_design_scores_gemma":[0.00007641184,0.0001964558,0.784007,0.0009853047,0.0004623871,0.0002132609,0.04206459,0.03248386,0.004556822,0.02592022,0.1088255,0.0002082419],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7588181,0.008033679,0.08713789,0.020377,0.0002162362,0.000668954,0.01243597,0.0003334869,0.1119786],"genre_scores_gemma":[0.9634137,0.001291333,0.01964325,0.0004993952,0.00002241387,0.0002924311,0.0012606,0.00004191969,0.01353506],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07723039,"threshold_uncertainty_score":0.1553704,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02816750289047402,"score_gpt":0.3000047312811266,"score_spread":0.2718372283906526,"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."}}