{"id":"W4402527095","doi":"10.1007/978-3-031-71736-9_3","title":"Knowledge Acquisition Passage Retrieval: Corpus, Ranking Models, and Evaluation Resources","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Topic Modeling","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek; Universiteit van Amsterdam; Canadian Institute of Steel Construction","keywords":"Computer science; Ranking (information retrieval); Information retrieval; Knowledge acquisition; Artificial intelligence; Natural language processing","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.002786865,0.0004798414,0.000436807,0.000950286,0.0002791767,0.001110195,0.001738217,0.000366179,0.00001098479],"category_scores_gemma":[0.00004738066,0.0004507002,0.00009724668,0.0006291274,0.0003681591,0.0009046991,0.001648784,0.0008039185,0.00002690305],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004535621,"about_ca_system_score_gemma":0.000392075,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000153201,"about_ca_topic_score_gemma":0.0000328377,"domain_scores_codex":[0.9955597,0.0000702015,0.0005414463,0.00193215,0.001380566,0.0005159338],"domain_scores_gemma":[0.9977389,0.0003843876,0.0002107849,0.001165282,0.0003577719,0.0001428707],"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":[0.000008857667,0.00001454425,0.00001053528,0.0001243845,0.00001602915,0.00004310206,0.004074987,0.1069685,0.0001674561,0.09825066,0.00001497983,0.790306],"study_design_scores_gemma":[0.0001395858,0.00003987573,0.00001238627,0.0005204492,0.00001587718,0.00003745761,1.841804e-7,0.5911821,0.0001312057,0.407244,0.0003919607,0.0002849496],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0006556651,0.009525032,0.9786324,0.0006588765,0.002050054,0.0005258906,0.000003383434,0.000271838,0.007676879],"genre_scores_gemma":[0.8118617,0.0002301236,0.1853283,0.0006035037,0.001072886,0.00001336784,0.000009078308,0.00006598917,0.000815073],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.811206,"threshold_uncertainty_score":0.9999267,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04013300437566199,"score_gpt":0.2820423754897383,"score_spread":0.2419093711140763,"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."}}