{"id":"W2131398643","doi":"10.1145/1368088.1368092","title":"Answering conceptual queries with Ferret","year":2008,"lang":"en","type":"article","venue":"","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":63,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Information retrieval; Data science","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":[0.00891828,0.00161466,0.001610605,0.004790029,0.003591066,0.007838292,0.003640586,0.003905636,0.02110338],"category_scores_gemma":[0.04388862,0.00139962,0.002651898,0.003768246,0.004040905,0.0283227,0.01073687,0.003733474,0.004142256],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003793535,"about_ca_system_score_gemma":0.002923883,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009517225,"about_ca_topic_score_gemma":0.01286607,"domain_scores_codex":[0.989791,0.003525449,0.000947194,0.002032241,0.003018107,0.0006859851],"domain_scores_gemma":[0.9799213,0.01172249,0.0007516114,0.004212386,0.002826174,0.0005660933],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009613946,0.0004236437,0.004186672,0.0008574625,0.0001670926,0.0007178716,0.005724024,0.01824401,0.004498431,0.6354035,0.06719786,0.261618],"study_design_scores_gemma":[0.000169317,0.00008681462,0.0004115285,0.0002214328,0.0001013718,0.0003557343,0.002256491,0.140612,0.008333469,0.7083753,0.1389618,0.0001147133],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01832522,0.0005310076,0.9459702,0.004456417,0.0003345277,0.0003525753,0.001873525,0.01182565,0.01633091],"genre_scores_gemma":[0.1966627,0.0008249053,0.771046,0.001845288,0.0003300749,0.0006543201,0.006964282,0.00259142,0.01908097],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02110338,"threshold_uncertainty_score":0.07059783,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03314523179199017,"score_gpt":0.2133362247023772,"score_spread":0.180190992910387,"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."}}