{"id":"W2077174673","doi":"10.1007/s11219-010-9112-9","title":"A comparative study for estimating software development effort intervals","year":2010,"lang":"en","type":"article","venue":"Software Quality Journal","topic":"Software Engineering Research","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"National Research Council Canada; Türkiye Bilimsel ve Teknolojik Araştırma Kurumu","keywords":"Computer science; Estimation; Software; Interval estimation; Interval (graph theory); Point (geometry); Point estimation; Data mining; Cluster (spacecraft); Machine learning; Statistics; Confidence interval; Engineering; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.005308088,0.0003354223,0.0005751696,0.0002866951,0.000769753,0.0007706695,0.001939867,0.0001160481,0.00004151208],"category_scores_gemma":[0.006972816,0.0003017668,0.0001893072,0.0004253848,0.00007255397,0.0007478031,0.0005355934,0.001263742,0.00005351538],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001795639,"about_ca_system_score_gemma":0.0006495728,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001304527,"about_ca_topic_score_gemma":0.0000460884,"domain_scores_codex":[0.9964195,0.0001880406,0.001004657,0.0005708791,0.001051273,0.0007656082],"domain_scores_gemma":[0.9943773,0.003227397,0.0003574182,0.0007635722,0.0008377911,0.0004365356],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001472504,0.002268085,0.5921211,0.0004525614,0.0009207936,0.0001618214,0.08122735,0.00271942,0.000856224,0.0009051199,0.005626958,0.3125933],"study_design_scores_gemma":[0.009780193,0.002685521,0.925217,0.0006444354,0.00008820319,0.001372578,0.003527198,0.02047654,0.007742492,0.01246911,0.01222488,0.003771825],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4121888,0.00002135994,0.5855163,0.0000733561,0.001314805,0.0004781139,0.000003654624,0.0003992131,0.000004375589],"genre_scores_gemma":[0.3688814,2.06677e-7,0.6306214,0.00005086382,0.0002446324,0.0001008639,0.000003193084,0.00002147847,0.00007596906],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3330959,"threshold_uncertainty_score":0.9999434,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08125202920087467,"score_gpt":0.3986317212269463,"score_spread":0.3173796920260716,"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."}}