{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002152414,0.0002575423,0.0003030715,0.00004091137,0.0001746835,0.00001169468,0.0004744698,0.0003639006,0.0001201897],"category_scores_gemma":[0.00003753197,0.0001579173,0.00006558449,0.00008403618,0.0006634898,0.000002102182,0.0001748162,0.0002107847,0.00005911343],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001812619,"about_ca_system_score_gemma":0.00007502131,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000154418,"about_ca_topic_score_gemma":0.0008441536,"domain_scores_codex":[0.9984875,0.00007039258,0.0002675345,0.0005197615,0.0001214957,0.0005333247],"domain_scores_gemma":[0.9990866,0.00006502789,0.0001350058,0.000579867,0.0000360011,0.0000974673],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.004958134,0.001260067,0.2405017,0.0001392634,0.001891715,0.000236531,0.006594177,0.00001027924,0.3353294,0.04178184,0.01891825,0.3483786],"study_design_scores_gemma":[0.006314485,0.00478798,0.3828214,0.00004891759,0.00035032,0.001247555,0.003473657,0.0000257988,0.1509307,0.005111912,0.4430369,0.001850377],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9586211,0.0009988584,0.003565246,0.0006450494,0.0002897297,0.0003407576,0.000004583488,0.00008008126,0.03545463],"genre_scores_gemma":[0.9922034,0.00007956092,0.005664161,0.001315055,0.000151171,0.0001292911,0.0000309616,0.00002409384,0.0004023541],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4241186,"threshold_uncertainty_score":0.6439679,"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."}}