{"id":"W145476732","doi":"10.7202/1029182ar","title":"Musées et médias sociaux","year":2015,"lang":"fr","type":"article","venue":"Documentation et bibliothèques","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Heritage","funders":"","keywords":"Humanities; Political science; Publics; Art","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":["metaepi_narrow","scholarly_communication"],"consensus_categories":["scholarly_communication"],"category_scores_codex":[0.002098928,0.0003277273,0.0003049989,0.001391683,0.0001922694,0.01152022,0.0004742003,0.0001802281,0.0008720352],"category_scores_gemma":[0.0002611649,0.0003601668,0.0001424553,0.003379364,0.0001982868,0.0155279,0.0002542935,0.0003369619,0.0006830996],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002300154,"about_ca_system_score_gemma":0.0008473178,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002132135,"about_ca_topic_score_gemma":0.0001850375,"domain_scores_codex":[0.9967411,0.0008654501,0.0004949717,0.0006036353,0.0007496099,0.0005451923],"domain_scores_gemma":[0.9979893,0.0003283805,0.0002877553,0.0005232339,0.0004950947,0.0003762573],"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":[0.00001955612,0.000230742,0.001939674,0.00009041515,0.00008579949,0.00007452878,0.02880679,0.00136639,0.000510744,0.6308841,0.3014558,0.0345355],"study_design_scores_gemma":[0.002858809,0.0006993177,0.008852106,0.0007020714,0.0001215406,0.0002533243,0.006234374,0.01917245,0.009627705,0.08456994,0.8653955,0.001512869],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1036751,0.05495389,0.5850899,0.1219231,0.01056528,0.0008435854,0.00004481475,0.00129782,0.1216066],"genre_scores_gemma":[0.8756286,0.008013926,0.04140333,0.02137841,0.0008192189,0.00005045766,0.00006949919,0.00008967282,0.0525469],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7719535,"threshold_uncertainty_score":0.999885,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1166774037718503,"score_gpt":0.3836151855377355,"score_spread":0.2669377817658852,"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."}}