{"id":"W2267083253","doi":"10.48550/arxiv.1508.00032","title":"A Neuro-Fuzzy Model with SEER-SEM for Software Effort Estimation","year":2015,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Software Engineering Research","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Estimation; Software; Fuzzy logic; Software sizing; Data mining; Software system; Machine learning; Artificial intelligence; Software construction; Systems engineering; Engineering","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":[0.002808757,0.0008177314,0.000864364,0.001017727,0.000551814,0.001290442,0.001697978,0.001488294,0.001769411],"category_scores_gemma":[0.005899698,0.0004043564,0.0009178686,0.001173076,0.0006037666,0.00125648,0.0007818912,0.001313115,0.0004071976],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001306736,"about_ca_system_score_gemma":0.001164537,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01222427,"about_ca_topic_score_gemma":0.01375444,"domain_scores_codex":[0.9986261,0.0006239002,0.00006471737,0.0003154354,0.0002886384,0.00008112285],"domain_scores_gemma":[0.998136,0.001094073,0.000188732,0.0001041063,0.0004381239,0.0000389689],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001140926,0.0001112549,0.002410409,0.0000905969,0.0001004725,0.0001073749,0.0001416454,0.9378064,0.0008355069,0.01566451,0.0005825176,0.04203527],"study_design_scores_gemma":[0.000003857539,0.00002569281,0.0002712226,0.000006586641,0.00001042904,0.00001082262,0.00000982355,0.9971322,0.000134602,0.00222912,0.0001584209,0.0000072446],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04434782,0.0003141377,0.9504672,0.0003553111,0.00005071096,0.0001182605,0.0001356468,0.000254995,0.003955929],"genre_scores_gemma":[0.8322009,0.0003399774,0.1639302,0.0001197754,0.00004377609,0.0003148496,0.0001594226,0.00001915415,0.002871906],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01222427,"threshold_uncertainty_score":0.02430618,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08030853294532818,"score_gpt":0.2132607882630032,"score_spread":0.132952255317675,"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."}}