{"id":"W1922429605","doi":"","title":"Parthenay... entre passé médiéval et avenir numérique","year":2008,"lang":"fr","type":"article","venue":"Commposite","topic":"Cultural Insights and Digital Impacts","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Humanities; Political science; Art","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001470416,0.0003936607,0.0003697337,0.00006638342,0.0004013814,0.00108655,0.0007328491,0.000195274,0.000183694],"category_scores_gemma":[0.00004601834,0.0003209744,0.0002707913,0.0004217218,0.0003002026,0.00324873,0.0005787703,0.0004164053,0.001155957],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001074547,"about_ca_system_score_gemma":0.0002104974,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009053526,"about_ca_topic_score_gemma":0.0003331925,"domain_scores_codex":[0.9977688,0.000227165,0.0003963755,0.0005090171,0.0003813462,0.0007173308],"domain_scores_gemma":[0.9981551,0.000179772,0.0001510757,0.0007778432,0.00020023,0.0005359848],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005123297,0.0009947618,0.002991956,0.0000902629,0.0001454553,0.005455234,0.04263583,0.0004661723,0.001908049,0.6745908,0.2570834,0.01358684],"study_design_scores_gemma":[0.0007116163,0.0003296396,0.009777771,0.0002661402,0.00001416664,0.004757314,0.00007643313,0.005103365,0.004467041,0.007959523,0.9658232,0.0007137689],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.166683,0.0207787,0.02161801,0.08141686,0.004532461,0.000585979,0.00007803529,0.0005092681,0.7037976],"genre_scores_gemma":[0.8871872,0.001130003,0.001304945,0.005659851,0.000391731,0.000008150024,0.00002655442,0.0000271931,0.1042644],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7205042,"threshold_uncertainty_score":0.9999504,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2248733130061199,"score_gpt":0.3023016367052992,"score_spread":0.07742832369917932,"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."}}