{"id":"W2805247950","doi":"10.4000/bssg.222","title":"Présentation de six bases de données portant sur les arts et la culture","year":2018,"lang":"fr","type":"article","venue":"Biens Symboliques / Symbolic Goods","topic":"Cultural Insights and Digital Impacts","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Art; Political science","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":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0009136198,0.0006966166,0.0006096605,0.0001934127,0.0005169977,0.002520657,0.0007750748,0.0005437751,0.0001262674],"category_scores_gemma":[0.000662802,0.0005565807,0.0003238317,0.0007318485,0.00072646,0.003187631,0.0003754942,0.0005257832,0.0001499241],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002167243,"about_ca_system_score_gemma":0.0007437603,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003829123,"about_ca_topic_score_gemma":0.001839679,"domain_scores_codex":[0.9957972,0.0007313355,0.0006496843,0.0008667069,0.0005581601,0.001396964],"domain_scores_gemma":[0.9970728,0.0003167561,0.0003673332,0.0007527913,0.0006920485,0.0007982594],"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":[0.00005507459,0.0009054648,0.006082547,0.0003231303,0.0001794574,0.0007070865,0.06471033,0.00004467252,0.01658713,0.7590764,0.1269264,0.02440231],"study_design_scores_gemma":[0.001038082,0.001528546,0.109251,0.002009301,0.0001749953,0.004163911,0.001727617,0.00513693,0.0531909,0.03350763,0.7864523,0.001818815],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.781576,0.008923142,0.01450474,0.02490586,0.003183658,0.0008446984,0.0002188392,0.0009508547,0.1648922],"genre_scores_gemma":[0.9621658,0.001774469,0.007982557,0.005414933,0.002557606,0.00003380957,0.00005599084,0.00006947514,0.01994532],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7255688,"threshold_uncertainty_score":0.9996886,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1707450747579486,"score_gpt":0.3576309751703914,"score_spread":0.1868859004124428,"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."}}