{"id":"W2394512509","doi":"10.7202/1036318ar","title":"Introduction : le cabinet d’Eurostudia","year":2016,"lang":"fr","type":"article","venue":"Eurostudia","topic":"Cultural Insights and Digital Impacts","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Cabinet (room); Engineering; Mechanical engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002172439,0.0003608171,0.0003285564,0.00007449464,0.0002911656,0.0008160613,0.0004784236,0.00009967374,0.0001946003],"category_scores_gemma":[0.0005633076,0.0002279784,0.0001483312,0.000616457,0.0003246773,0.003835071,0.0005820752,0.000161438,0.00333428],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001035614,"about_ca_system_score_gemma":0.0001988789,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003074607,"about_ca_topic_score_gemma":0.000096753,"domain_scores_codex":[0.9975437,0.0001488263,0.0003886923,0.0008264687,0.0003664451,0.0007258522],"domain_scores_gemma":[0.9983677,0.000132331,0.0001556245,0.0007216572,0.000284626,0.0003381238],"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":[0.00001377115,0.0002377833,0.0001915301,0.00001559431,0.00003575097,0.0001701067,0.0006676325,0.00000592138,0.0127683,0.226269,0.709478,0.0501466],"study_design_scores_gemma":[0.0007653861,0.0003120734,0.02767822,0.00006751636,0.00001878794,0.0002634632,0.00002453328,0.00003816159,0.003945022,0.002735795,0.9637427,0.0004083217],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.03436297,0.01958624,0.01466399,0.6730266,0.03535248,0.0007032647,0.0001260966,0.0005789037,0.2215995],"genre_scores_gemma":[0.5730291,0.001216862,0.0004648628,0.001816565,0.005356097,0.000009834645,0.00000488916,0.0000396367,0.4180622],"genre_candidate":"commentary","genre_consensus":null,"teacher_disagreement_score":0.6712101,"threshold_uncertainty_score":0.9974418,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1626404858862755,"score_gpt":0.2734795929612084,"score_spread":0.1108391070749329,"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."}}