{"id":"W7113586993","doi":"","title":"Proceedings of the workshop \"Information Extraction meets Corpus LInguistics\"","year":2000,"lang":"en","type":"other","venue":"Research Explorer (The University of Manchester)","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Open Text (Canada)","funders":"","keywords":"Extraction (chemistry); Information extraction; Feature extraction; Class (philosophy)","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":[0.005137688,0.001441602,0.00212882,0.003781363,0.001542503,0.007443175,0.002320719,0.001523758,0.09973133],"category_scores_gemma":[0.01087046,0.001024128,0.001026545,0.004010758,0.001370243,0.006841571,0.003335902,0.002434397,0.03844742],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001313521,"about_ca_system_score_gemma":0.002357546,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00956079,"about_ca_topic_score_gemma":0.01264057,"domain_scores_codex":[0.9979656,0.001157818,0.0001141673,0.0002585158,0.0004031378,0.0001007462],"domain_scores_gemma":[0.9909899,0.005597584,0.0001433556,0.001515444,0.001072633,0.0006810326],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002471745,0.0001578398,0.0002310673,0.0004742685,0.00004418361,0.0001151615,0.000843197,0.0004009255,0.001656323,0.015914,0.6515639,0.3283519],"study_design_scores_gemma":[0.00007835624,0.00003448219,0.0007251638,0.0002360627,0.00006683743,0.0002325506,0.0002958403,0.004547406,0.00394277,0.0197571,0.9700542,0.00002925997],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.01088863,0.01943966,0.5654795,0.02782856,0.01205231,0.001016528,0.02180109,0.03014484,0.311349],"genre_scores_gemma":[0.04101504,0.01387301,0.2936113,0.003535426,0.003653954,0.0009091842,0.06093775,0.02372569,0.5587386],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.09973133,"threshold_uncertainty_score":0.3336345,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05883746823935579,"score_gpt":0.295294345273203,"score_spread":0.2364568770338472,"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."}}