{"id":"W2588137444","doi":"10.29173/cais356","title":"Integrating Knowledge from Different Sources for Automatic Back-of-the-book Indexing","year":2013,"lang":"fr","type":"article","venue":"Proceedings of the Annual Conference of CAIS / Actes du congrès annuel de l ACSI","topic":"Advanced Text Analysis Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Indexation; Search engine indexing; Computer science; Valuation (finance); Humanities; Information retrieval; Library science; Art; Business","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":[0.005614433,0.001189571,0.001704122,0.02816284,0.001904647,0.008123061,0.002202568,0.00176812,0.01101338],"category_scores_gemma":[0.03522473,0.0008058068,0.001666607,0.01975936,0.001313484,0.01100026,0.005460512,0.001716048,0.00656038],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001806107,"about_ca_system_score_gemma":0.003769865,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006160803,"about_ca_topic_score_gemma":0.01025894,"domain_scores_codex":[0.9939214,0.001814069,0.0006394965,0.001025175,0.002288927,0.0003109954],"domain_scores_gemma":[0.973453,0.01584296,0.001019624,0.004519273,0.004723946,0.0004412186],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002699626,0.0002749636,0.00435697,0.001093961,0.000223285,0.000406344,0.001940477,0.002101428,0.008428653,0.00721752,0.005614522,0.9680721],"study_design_scores_gemma":[0.0005572875,0.000962329,0.0449818,0.003024648,0.002510019,0.004023703,0.01306576,0.1976967,0.1783189,0.1626012,0.3912245,0.001033096],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0756041,0.00641689,0.8414509,0.001605301,0.0003731087,0.001741102,0.005632594,0.01018114,0.05699485],"genre_scores_gemma":[0.1820123,0.003255935,0.7930004,0.0002698456,0.0002584799,0.0005801158,0.01000259,0.001387918,0.009232577],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02816284,"threshold_uncertainty_score":0.03684348,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02689134641143379,"score_gpt":0.2663705937485865,"score_spread":0.2394792473371527,"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."}}