{"id":"W4244478245","doi":"10.1115/1.4037817","title":"Automated Extraction of Function Knowledge From Text","year":2017,"lang":"en","type":"article","venue":"Journal of Mechanical Design","topic":"Software Engineering Research","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"Autodesk (Canada)","funders":"Oregon State University","keywords":"WordNet; Computer science; Natural language processing; Word2vec; Knowledge base; Parsing; Function (biology); Artificial intelligence; Information retrieval; Knowledge extraction; Artifact (error); Information extraction","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":[0.00129685,0.001697049,0.0009882047,0.01753793,0.0009750117,0.002200155,0.001409142,0.0009943339,0.004141623],"category_scores_gemma":[0.009349975,0.0006125207,0.001249983,0.007230213,0.0007286143,0.004731256,0.001599888,0.001000016,0.003672172],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001013804,"about_ca_system_score_gemma":0.00248869,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003248655,"about_ca_topic_score_gemma":0.00463501,"domain_scores_codex":[0.9981391,0.0003463466,0.0002637446,0.0005019815,0.0006488894,0.00009991533],"domain_scores_gemma":[0.992075,0.004350832,0.0008535992,0.0008556927,0.001751268,0.0001136049],"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":[0.0001552951,0.0002233758,0.005551849,0.002403568,0.0001277875,0.00182415,0.001481831,0.004172614,0.03141342,0.00935501,0.03144139,0.9118497],"study_design_scores_gemma":[0.0001605012,0.0003671307,0.03517908,0.00241658,0.0006844908,0.004828234,0.00461306,0.2525596,0.1814428,0.08508243,0.4322899,0.0003762052],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08867414,0.003741989,0.8113605,0.001291426,0.0002804873,0.001268767,0.04450981,0.02978389,0.01908896],"genre_scores_gemma":[0.15739,0.00203679,0.7553108,0.0002385755,0.0001655629,0.0007540587,0.0778993,0.001198531,0.005006357],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01753793,"threshold_uncertainty_score":0.0138551,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05922149367044244,"score_gpt":0.329604316374932,"score_spread":0.2703828227044895,"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."}}