{"id":"W1419069490","doi":"10.4018/978-1-4666-4785-5.ch014","title":"Improving the Performance of Neuro-Fuzzy Function Point Backfiring Model with Additional Environmental Factors","year":2013,"lang":"en","type":"book-chapter","venue":"Advances in computational intelligence and robotics book series","topic":"Software Engineering Research","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Fuzzy logic; Point (geometry); Computer science; Artificial neural network; Function (biology); Function point; Code (set theory); Software; Neuro-fuzzy; Mathematical optimization; Algorithm; Artificial intelligence; Mathematics; Software development; Fuzzy control system","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":[0.001991248,0.001084202,0.001060649,0.0007465453,0.0004352226,0.001363465,0.00136792,0.001128858,0.001655582],"category_scores_gemma":[0.005531174,0.0003559045,0.0007992933,0.0005956134,0.0003090202,0.0016497,0.0006734373,0.001117335,0.0005383362],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001174703,"about_ca_system_score_gemma":0.001188518,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03641871,"about_ca_topic_score_gemma":0.01621333,"domain_scores_codex":[0.9993975,0.0001705853,0.00003796303,0.0001651119,0.0001619537,0.00006682063],"domain_scores_gemma":[0.9978194,0.001261423,0.0001307338,0.0001450735,0.0006035828,0.00003984357],"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":[0.0003081335,0.0001753297,0.004412302,0.0001193813,0.000104591,0.00006672369,0.0001380482,0.8307384,0.002474321,0.001259513,0.00101705,0.1591863],"study_design_scores_gemma":[0.000003822575,0.00002635875,0.0004229704,0.000004392261,0.00001000603,0.000005752079,0.00001076051,0.998668,0.000483317,0.0002391835,0.0001197067,0.0000057646],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3161087,0.001624606,0.6716796,0.000555373,0.0001289247,0.000106577,0.0001763386,0.001623654,0.007996277],"genre_scores_gemma":[0.9429206,0.0003271467,0.05356952,0.00008415406,0.0000249159,0.00005773403,0.000222106,0.00005647299,0.002737403],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03641871,"threshold_uncertainty_score":0.07241344,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.014925922676088,"score_gpt":0.215956003084556,"score_spread":0.201030080408468,"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."}}