{"id":"W2240090265","doi":"","title":"Derive: finding semantic concepts with property-values from natural language text","year":2014,"lang":"en","type":"article","venue":"Computer Science and Software Engineering","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Property (philosophy); Computer science; Natural language; Natural language processing; Property value; Information retrieval; Artificial intelligence; Matching (statistics); Semantic compression; Semantic computing; Semantic matching; Value (mathematics); Natural (archaeology); Semantic technology; Semantic Web; Mathematics; Machine learning","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":[0.000404321,0.0001905207,0.0002114967,0.0001448241,0.0002463937,0.000522493,0.0009661277,0.00003628157,0.000001461004],"category_scores_gemma":[0.0002585269,0.0001206082,0.00002397648,0.0004750137,0.0002094436,0.0009011534,0.0004511955,0.000147242,0.000008976166],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003136355,"about_ca_system_score_gemma":0.00006681625,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005468292,"about_ca_topic_score_gemma":0.000005765309,"domain_scores_codex":[0.9984604,0.00002254146,0.0001380529,0.0005614455,0.0004019723,0.0004155927],"domain_scores_gemma":[0.9990266,0.0002796948,0.00004313817,0.0004351864,0.00009736499,0.0001180281],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00000739285,0.00004034002,0.02067812,0.0001087164,0.00004815097,0.0001322981,0.01475584,0.007233896,0.006588086,0.003536107,0.0003146814,0.9465564],"study_design_scores_gemma":[0.000265339,0.00009180928,0.03949507,0.0001493693,0.00000582555,0.00005254701,0.00005478137,0.9561069,0.003012754,0.00006973092,0.0003648498,0.0003310246],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3257885,0.0003483376,0.6728804,0.00009686583,0.0004554162,0.00006668569,3.05513e-7,0.0003513559,0.00001219333],"genre_scores_gemma":[0.7088639,0.000004148702,0.2908104,0.0001826306,0.0001080005,0.000003686485,6.542299e-7,0.000006541601,0.0000199852],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.948873,"threshold_uncertainty_score":0.5038412,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0070147636511661,"score_gpt":0.2232628817727366,"score_spread":0.2162481181215705,"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."}}