{"id":"W4408840153","doi":"10.1007/978-3-031-73143-3_1","title":"Handbook on Natural Language Processing for Requirements Engineering: Overview","year":2025,"lang":"en","type":"book-chapter","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Natural (archaeology); Software engineering; History; Archaeology","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":[0.0008447659,0.00148041,0.001294832,0.004290674,0.0005799391,0.003589482,0.001853306,0.001136622,0.06739727],"category_scores_gemma":[0.002937152,0.001127587,0.00116375,0.007029504,0.0007228649,0.004121187,0.001191952,0.002269463,0.0581669],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001059801,"about_ca_system_score_gemma":0.001891107,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002146196,"about_ca_topic_score_gemma":0.004098329,"domain_scores_codex":[0.9992768,0.00008182525,0.0000765619,0.0001189768,0.0004146582,0.00003114748],"domain_scores_gemma":[0.9982548,0.001028483,0.00005574036,0.0001718257,0.0004465733,0.00004248737],"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":[0.00001749531,0.00005381586,0.00005520472,0.00184636,0.00001782366,0.00007947577,0.0001929464,0.001248508,0.003278446,0.03331872,0.2797972,0.680094],"study_design_scores_gemma":[0.00000649158,0.00001280217,0.0001748707,0.000439065,0.00001168224,0.0003173561,0.00004503227,0.00102054,0.000980156,0.0264621,0.9705114,0.00001848533],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.001118702,0.1265847,0.5220996,0.002930474,0.002577051,0.000585209,0.007130125,0.01335179,0.3236223],"genre_scores_gemma":[0.007191511,0.1235805,0.5437723,0.00217962,0.001551808,0.0008615168,0.01725546,0.004841523,0.2987657],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.06739727,"threshold_uncertainty_score":0.2254663,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03610718233777591,"score_gpt":0.3094514654281686,"score_spread":0.2733442830903927,"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."}}