{"id":"W1971245915","doi":"10.1353/ils.2010.0004","title":"Finding Images in an Online Public Access Catalogue: Analysis of User Queries, Subject Headings, and Description Notes / Le repérage d'images dans un catalogue en ligne à accès libre : analyse des requêtes des utilisateurs, des vedettes-matière, et des notes descriptives","year":2010,"lang":"fr","type":"article","venue":"Canadian Journal of Information and Library Science","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Public access; Subject (documents); Information retrieval; Computer science; Subject access; Rage (emotion); World Wide Web; Library science; Psychology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.002213817,0.0002419881,0.0004862584,0.01381502,0.0008268972,0.002748413,0.0004935641,0.0005754502,0.003303322],"category_scores_gemma":[0.02542274,0.0002266307,0.0006356952,0.01313303,0.0006086601,0.003368192,0.001029623,0.000358811,0.001748111],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001071097,"about_ca_system_score_gemma":0.0008899994,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02611544,"about_ca_topic_score_gemma":0.02852452,"domain_scores_codex":[0.9973456,0.0004783301,0.0004805284,0.0002089949,0.001288141,0.0001984467],"domain_scores_gemma":[0.9646207,0.02447277,0.003287647,0.001231401,0.005794826,0.0005927479],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001814045,0.0004694689,0.5945575,0.001840631,0.000215851,0.001491564,0.02724435,0.001332361,0.02909997,0.002652673,0.008095293,0.3311864],"study_design_scores_gemma":[0.00002864806,0.000353957,0.935713,0.000200906,0.0002611937,0.002207375,0.02092604,0.00877248,0.0125243,0.0007437701,0.01813129,0.0001370024],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9879901,0.000708764,0.003297793,0.0001743212,0.000011388,0.0001269659,0.003068228,0.0004371868,0.004185246],"genre_scores_gemma":[0.9712968,0.0008310528,0.01267421,0.00007191274,0.00002369475,0.000123789,0.009463743,0.0002290787,0.005285721],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9972516,"threshold_uncertainty_score":0.05192685,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05417928275041749,"score_gpt":0.2791582739694417,"score_spread":0.2249789912190241,"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."}}