{"id":"W7020286044","doi":"","title":"Les Besoins en information électronique des grandes corporations québécoises : rapport synthèse : étude de marché /","year":2015,"lang":"fr","type":"other","venue":"Bibliothèque et Archives nationales du Québec (Québec government)","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Context (archaeology); Information system; Government (linguistics); Information center","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":[0.005252043,0.0003725723,0.0004672559,0.005979247,0.0062417,0.01124147,0.0009983843,0.001345969,0.01397442],"category_scores_gemma":[0.02559151,0.0003310987,0.0003545421,0.01949022,0.004458858,0.005990447,0.003039556,0.00201234,0.0007727554],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05418702,"about_ca_system_score_gemma":0.04073914,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.893934,"about_ca_topic_score_gemma":0.916315,"domain_scores_codex":[0.9952933,0.001216793,0.0001806624,0.0004011097,0.002172454,0.0007355911],"domain_scores_gemma":[0.9728917,0.01339299,0.002837022,0.0008813445,0.008917446,0.001079479],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000234577,0.0001064404,0.1297079,0.003017002,0.0001522881,0.001167868,0.3621446,0.001219236,0.0009937354,0.164476,0.1048271,0.2319532],"study_design_scores_gemma":[0.0000113132,0.00005059102,0.2662717,0.004248228,0.0001166963,0.000343101,0.2208516,0.0006611796,0.001078622,0.006978802,0.4993153,0.00007293803],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"other","genre_scores_codex":[0.5421225,0.07484476,0.003067593,0.06199468,0.0003584353,0.0001615743,0.007247308,0.0001174914,0.3100857],"genre_scores_gemma":[0.9102998,0.02157837,0.001314758,0.001488523,0.0000795246,0.00007729496,0.001155675,0.00007652548,0.06392942],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.106066,"threshold_uncertainty_score":0.3931562,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0170922186327239,"score_gpt":0.2362294511255585,"score_spread":0.2191372324928346,"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."}}