{"id":"W2724181509","doi":"10.29173/cais180","title":"PériCulture2 : utilisation du péritexte pour l’indexation automatique des objets multimédias","year":2013,"lang":"fr","type":"article","venue":"Proceedings of the Annual Conference of CAIS / Actes du congrès annuel de l ACSI","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Indexation; Humanities; Search engine indexing; Computer science; Art; Information retrieval","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002320044,0.001121306,0.001384112,0.004533712,0.000880647,0.003446058,0.001374374,0.001006694,0.005796363],"category_scores_gemma":[0.009673706,0.0004467864,0.0009126994,0.003592387,0.0008755412,0.003466656,0.002036519,0.000877648,0.003023377],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005919148,"about_ca_system_score_gemma":0.0008343676,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004677936,"about_ca_topic_score_gemma":0.004243912,"domain_scores_codex":[0.9971184,0.0006455284,0.0002647433,0.0005020747,0.001284598,0.0001847205],"domain_scores_gemma":[0.9918634,0.00388542,0.0003231086,0.001804989,0.001853369,0.000269623],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001354338,0.0002732019,0.003156623,0.00105421,0.0001097467,0.0003080081,0.002070473,0.005145966,0.1518101,0.002705063,0.004926574,0.8270857],"study_design_scores_gemma":[0.0003875641,0.002472079,0.0303666,0.0001739648,0.0002228393,0.002780606,0.002317842,0.2703775,0.5866268,0.006135487,0.09776082,0.0003777632],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.227869,0.002649275,0.7307075,0.000284875,0.0003175061,0.0006531135,0.002886785,0.02361772,0.01101419],"genre_scores_gemma":[0.2577904,0.0006486902,0.7251347,0.00009270774,0.00010423,0.0004728911,0.003475104,0.00216709,0.01011419],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005796363,"threshold_uncertainty_score":0.01939082,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02790648919476617,"score_gpt":0.2409120721927576,"score_spread":0.2130055829979915,"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."}}