{"id":"W1984499625","doi":"10.1002/meet.145044031","title":"Study on the influence of vocabularies used for image indexing in a multilingual retrieval environment","year":2007,"lang":"en","type":"article","venue":"Proceedings of the American Society for Information Science and Technology","topic":"Open Education and E-Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Search engine indexing; Computer science; Information retrieval; Context (archaeology); Vocabulary; Image retrieval; Multilingualism; Controlled vocabulary; World Wide Web; Artificial intelligence; Image (mathematics); Linguistics; Geography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"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":[0.01069493,0.0005643094,0.0007918971,0.002248393,0.001147635,0.004314041,0.0009338888,0.0006630935,0.00159291],"category_scores_gemma":[0.09999423,0.0002928416,0.0006691173,0.002071606,0.001312297,0.003253909,0.001427669,0.0006959039,0.0003951762],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00135043,"about_ca_system_score_gemma":0.001167903,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0101842,"about_ca_topic_score_gemma":0.005351049,"domain_scores_codex":[0.9895948,0.00544592,0.00152859,0.000761991,0.002170885,0.0004977529],"domain_scores_gemma":[0.819473,0.1555273,0.009568541,0.00359959,0.009984932,0.001846671],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0160752,0.005383335,0.4882176,0.003065247,0.001323838,0.002589601,0.01344931,0.03504777,0.1967634,0.005648365,0.002206205,0.2302302],"study_design_scores_gemma":[0.0009412964,0.01788948,0.5524538,0.000586529,0.004032601,0.002922211,0.03187655,0.2165485,0.1594576,0.00434617,0.008197975,0.0007473722],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.9947304,0.0005533487,0.002403681,0.0000690083,0.00001516142,0.0001106882,0.00008837033,0.00005102357,0.001978231],"genre_scores_gemma":[0.9980835,0.0001331069,0.001390818,0.00001526888,0.00001142192,0.00003624207,0.0001226989,0.00002772005,0.0001792096],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.01069493,"threshold_uncertainty_score":0.05656087,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01368234509264654,"score_gpt":0.2889202325248921,"score_spread":0.2752378874322456,"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."}}