{"id":"W2068154757","doi":"10.1108/07378831211285103","title":"TIIARA: the “making of” a bilingual taxonomy for retrieval of digital images","year":2012,"lang":"en","type":"article","venue":"Library Hi Tech","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Card sorting; Taxonomy (biology); Computer science; Usability; Originality; Process (computing); Structuring; Information retrieval; Data science; Human–computer interaction; Task (project management); Qualitative research; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001859958,0.00009265867,0.0001434376,0.00007780002,0.00005506099,0.00008036019,0.0008142916,0.00005432538,0.00001069014],"category_scores_gemma":[0.00009163993,0.00006274278,0.0001033925,0.0004562712,0.0001149125,0.002040011,0.0002508509,0.00008176122,0.00000353951],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006277547,"about_ca_system_score_gemma":0.00009580434,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":3.190787e-7,"about_ca_topic_score_gemma":7.02959e-9,"domain_scores_codex":[0.9992101,0.00002131265,0.0002721576,0.0001484837,0.0001546533,0.0001932982],"domain_scores_gemma":[0.999118,0.0001751014,0.0001961482,0.0004408685,0.0000465307,0.00002332085],"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":[0.0002160201,0.0007829999,0.02036131,0.0004244511,0.0001074706,0.000002603865,0.001793599,7.059038e-7,0.07693523,0.4369268,0.02273155,0.4397172],"study_design_scores_gemma":[0.0001257354,0.000107147,0.0007791918,0.00003157876,0.000007368732,0.000008042021,0.0000770816,0.0004430501,0.9108012,0.004890071,0.08260158,0.0001279561],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005909897,0.0009376318,0.9814574,0.001770928,0.0001694478,0.0006840869,0.00005379707,0.0004255326,0.008591351],"genre_scores_gemma":[0.9372993,0.00000876273,0.0618287,0.00009503392,0.00009003968,0.00002090381,0.000004697409,0.00001023007,0.0006423728],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9313894,"threshold_uncertainty_score":0.2558576,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03908337827263465,"score_gpt":0.2703681334403763,"score_spread":0.2312847551677417,"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."}}