{"id":"W2117904965","doi":"10.1145/1242572.1242787","title":"Ontology engineering using volunteer labor","year":2007,"lang":"en","type":"article","venue":"","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Michael Smith Health Research BC; Genome Canada","keywords":"Correctness; Ontology; Computer science; Voting; Domain (mathematical analysis); Aggregate (composite); Ontology engineering; Protégé; Artificial intelligence; Knowledge management; Domain knowledge; Process ontology; Semantic Web; Algorithm","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":[0.01419843,0.000840653,0.0006936986,0.001435993,0.002036784,0.002370871,0.002350334,0.001149422,0.009498257],"category_scores_gemma":[0.03393965,0.0004879006,0.0007132632,0.0009852288,0.001810303,0.00448925,0.005813091,0.001519618,0.00315502],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001035364,"about_ca_system_score_gemma":0.003229138,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001974998,"about_ca_topic_score_gemma":0.003134445,"domain_scores_codex":[0.9892396,0.006357651,0.0004221755,0.001645714,0.001712897,0.0006220474],"domain_scores_gemma":[0.9701768,0.01129567,0.001834428,0.01083309,0.00403379,0.001826353],"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":[0.00117499,0.002277771,0.016062,0.0005824553,0.0001272172,0.0006426205,0.01194364,0.01785099,0.0236021,0.1043053,0.01484772,0.8065832],"study_design_scores_gemma":[0.000590365,0.002725363,0.00820521,0.000375974,0.0001783562,0.001553801,0.009151966,0.2978026,0.0359678,0.3475872,0.2956231,0.0002383003],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08346496,0.0002076353,0.8898199,0.001801643,0.0001770232,0.001113013,0.00009659639,0.001436631,0.02188255],"genre_scores_gemma":[0.6059018,0.0002196152,0.3718605,0.0007587199,0.0001344747,0.001741333,0.0004237427,0.0003133199,0.01864652],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01419843,"threshold_uncertainty_score":0.0750894,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01767963606670661,"score_gpt":0.2573621271525236,"score_spread":0.239682491085817,"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."}}