{"id":"W7115938177","doi":"10.60870/h11e-j508","title":"À la fine pointe du monde numérique : possibilités pour les institutions de la mémoire collective au Canada","year":2025,"lang":"","type":"report","venue":"CCA_Repo","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Context (archaeology); Government (linguistics); Field (mathematics); Agency (philosophy)","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","sts","research_integrity"],"consensus_categories":["metaepi_narrow","sts","research_integrity"],"category_scores_codex":[0.005838843,0.002911317,0.00353161,0.00173645,0.003390114,0.0006247535,0.001876716,0.003598384,0.0007385485],"category_scores_gemma":[0.0314932,0.003190189,0.001635662,0.004705122,0.002948183,0.0004301302,0.001415797,0.006236247,0.0001420864],"about_ca_system_candidate":true,"about_ca_system_consensus":true,"about_ca_system_score_codex":0.1218449,"about_ca_system_score_gemma":0.7090545,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9358574,"about_ca_topic_score_gemma":0.9789464,"domain_scores_codex":[0.9822946,0.00472706,0.003764022,0.003685376,0.002730346,0.002798641],"domain_scores_gemma":[0.9821027,0.005499584,0.002770638,0.003462575,0.00461905,0.001545446],"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":[0.00158458,0.005510699,0.1282923,0.007022138,0.01205415,0.06358386,0.009290793,0.02085482,0.007057508,0.01676339,0.7217746,0.006211204],"study_design_scores_gemma":[0.003156515,0.0001806087,0.04962541,0.006496154,0.001321618,0.02283024,0.004884125,0.001111304,0.002272262,0.00177086,0.9029992,0.003351652],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1344041,0.00452573,0.001272922,0.001377617,0.002908034,0.003361639,0.005990071,0.000525261,0.8456346],"genre_scores_gemma":[0.748098,0.0008938967,0.0005088554,0.00007513633,0.001354917,0.0009041633,0.0007213723,0.0005083542,0.2469353],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.6136939,"threshold_uncertainty_score":0.9997652,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02274843245770886,"score_gpt":0.3001650077545394,"score_spread":0.2774165752968305,"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."}}