{"id":"W2007697117","doi":"10.1038/npre.2009.3970.1","title":"Overcoming the Ontology Enrichment Bottleneck with Quick Term Templates","year":2009,"lang":"en","type":"preprint","venue":"Nature Precedings","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Terry Fox Research Institute","funders":"","keywords":"Computer science; Ontology; Bottleneck; Domain (mathematical analysis); Task (project management); Process ontology; Scalability; Term (time); Template; Process (computing); Software engineering; Ontology-based data integration; Information retrieval; Domain knowledge; Programming language; Database; Systems 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00721448,0.0009502736,0.0009804986,0.003306683,0.001029369,0.003979215,0.002949839,0.001520248,0.007188844],"category_scores_gemma":[0.03610892,0.001335979,0.001208949,0.002923761,0.001237258,0.005881188,0.003745303,0.003189824,0.006244226],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008733901,"about_ca_system_score_gemma":0.002454384,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001131361,"about_ca_topic_score_gemma":0.00149384,"domain_scores_codex":[0.9941415,0.001761367,0.0008862392,0.0007844115,0.002206902,0.0002195737],"domain_scores_gemma":[0.9604938,0.01955839,0.002479866,0.01248296,0.004417737,0.0005672279],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0008190719,0.0007663637,0.004480383,0.001189189,0.0001708459,0.00191864,0.002143596,0.009001374,0.1368297,0.08123383,0.05371824,0.7077288],"study_design_scores_gemma":[0.0001807099,0.0002334393,0.001841129,0.0004220269,0.0002191795,0.003048211,0.0005245812,0.1210974,0.4269848,0.07501451,0.370158,0.0002760318],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01412698,0.0001499053,0.9558851,0.0005924443,0.0001890021,0.0004192154,0.0008063972,0.02449964,0.003331409],"genre_scores_gemma":[0.03753058,0.0001728544,0.9510582,0.0002621902,0.00006167489,0.0003171714,0.001801187,0.00481345,0.003982727],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00721448,"threshold_uncertainty_score":0.0381543,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01183715873429956,"score_gpt":0.2663852392022972,"score_spread":0.2545480804679977,"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."}}