{"id":"W7082159534","doi":"10.5281/zenodo.17149974","title":"Reliable Requirement Engineering Using LLM for Tag Governance","year":2025,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Artifact (error); Pipeline (software); Resource (disambiguation); Requirements analysis; Audit; Replicate; Requirements engineering; Reliability (semiconductor); Ontology","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":[0.006166578,0.001587743,0.0005548986,0.003133212,0.0008702601,0.002647956,0.002509497,0.001055079,0.01305049],"category_scores_gemma":[0.03291161,0.0007760034,0.001615237,0.002798133,0.0005257484,0.002957942,0.004148233,0.001631971,0.01927274],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002008392,"about_ca_system_score_gemma":0.002796797,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01259583,"about_ca_topic_score_gemma":0.03117813,"domain_scores_codex":[0.9934366,0.001982461,0.0009486178,0.001271902,0.002032834,0.0003277382],"domain_scores_gemma":[0.981425,0.004449514,0.000791036,0.0104207,0.002600614,0.0003131692],"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":[0.0003760775,0.0002144581,0.01242473,0.001646189,0.0001428217,0.0002817136,0.0004064997,0.01578087,0.003348355,0.01755695,0.8594508,0.08837043],"study_design_scores_gemma":[0.0002482997,0.0001120517,0.007702545,0.000447777,0.0000591947,0.0003487321,0.0005170334,0.1017189,0.0110858,0.04550759,0.8321388,0.0001132398],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.01291799,0.0003675236,0.1361436,0.001658056,0.000290781,0.000785229,0.7301164,0.09918636,0.01853406],"genre_scores_gemma":[0.01762871,0.0001048694,0.07272615,0.0003301633,0.00001454103,0.0007260515,0.9044588,0.001886965,0.002123783],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01305049,"threshold_uncertainty_score":0.04365826,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03123355314077932,"score_gpt":0.2403904573894465,"score_spread":0.2091569042486671,"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."}}