{"id":"W2172304783","doi":"10.3233/ao-2011-0086","title":"Overcoming the ontology enrichment bottleneck with Quick Term Templates","year":2011,"lang":"en","type":"article","venue":"Applied Ontology","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Cancer Agency","funders":"Biotechnology and Biological Sciences Research Council","keywords":"Computer science; Bottleneck; Term (time); Template; Ontology; Information retrieval; Programming language; Embedded system","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":[0.008651262,0.001471338,0.001468111,0.004422141,0.001133404,0.004955617,0.00316895,0.001617663,0.007557817],"category_scores_gemma":[0.04171726,0.00157807,0.002185519,0.004718022,0.0008067125,0.007465341,0.004094617,0.003912065,0.006871684],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001089276,"about_ca_system_score_gemma":0.004703758,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003142613,"about_ca_topic_score_gemma":0.005575337,"domain_scores_codex":[0.9940568,0.001740123,0.001299721,0.0007866703,0.001857055,0.0002596478],"domain_scores_gemma":[0.9559763,0.02449761,0.002287786,0.009894495,0.006679596,0.0006641757],"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":[0.000630639,0.0005007397,0.005628704,0.002072824,0.00026242,0.001258597,0.002109655,0.005837309,0.05884307,0.04299555,0.05664329,0.8232174],"study_design_scores_gemma":[0.0002523324,0.0003070201,0.003432929,0.0008648658,0.0006376278,0.003425176,0.001405345,0.1632337,0.1973428,0.1112394,0.517574,0.000284884],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01256916,0.0005326788,0.9541321,0.001265131,0.0002913582,0.0006269822,0.002636324,0.023626,0.004320389],"genre_scores_gemma":[0.03727899,0.0005218042,0.9473585,0.0005600344,0.00009539931,0.0003629326,0.005473502,0.003647277,0.004701528],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008651262,"threshold_uncertainty_score":0.04575276,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02257876027611021,"score_gpt":0.2409487465018414,"score_spread":0.2183699862257312,"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."}}