{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":["scholarly_communication"],"category_scores_codex":[0.002394146,0.0003440284,0.0005500949,0.003413984,0.001020178,0.005158521,0.001597585,0.0001788101,0.00003054086],"category_scores_gemma":[0.002659698,0.0003178657,0.000125663,0.006232226,0.005666827,0.08565208,0.0002677795,0.0005405802,6.430507e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001666751,"about_ca_system_score_gemma":0.002898026,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.02152147,"about_ca_topic_score_gemma":0.06667567,"domain_scores_codex":[0.9970179,0.0003545039,0.001208279,0.0003703242,0.0003834386,0.0006655145],"domain_scores_gemma":[0.9965293,0.0004047877,0.0009018591,0.000414613,0.0008426321,0.0009067525],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002845185,0.0000942709,0.7362021,0.0002467866,0.00008629331,0.00003257858,0.04867021,0.00005900947,0.02643713,0.004329294,0.00003959958,0.1837743],"study_design_scores_gemma":[0.0002390142,0.0002234564,0.8699919,0.0003483097,0.0000899752,0.0002059352,0.005145954,0.005425818,0.1148731,0.002844781,0.0002786574,0.0003331216],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9320562,0.002703515,0.06272543,0.001587073,0.0001372501,0.000175617,0.0004731734,0.00004558831,0.00009616566],"genre_scores_gemma":[0.9230905,0.001836354,0.07455617,0.0002167774,0.00003083421,0.000004016778,0.0002156785,0.0000141024,0.00003558823],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1834412,"threshold_uncertainty_score":0.9999273,"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."}}