{"id":"W3013691730","doi":"10.1002/smr.379","title":"Optimized staffing for product releases and its application at Chartwell Technology","year":2008,"lang":"en","type":"article","venue":"Journal of Software Maintenance and Evolution Research and Practice","topic":"Software Engineering Techniques and Practices","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Staffing; Heuristics; Computer science; Quality (philosophy); Feature (linguistics); Product (mathematics); Process (computing); Point (geometry); Genetic algorithm; Operations research; Resource (disambiguation); Mathematical optimization; Machine learning; Mathematics","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":[0.002367905,0.000705806,0.0005235862,0.001728371,0.0006428114,0.001015821,0.001002013,0.0007683091,0.004542611],"category_scores_gemma":[0.008308129,0.0003139809,0.0004570529,0.001527174,0.0003245547,0.0008501605,0.0004455234,0.0008821856,0.0003636797],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003148672,"about_ca_system_score_gemma":0.002336777,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03002119,"about_ca_topic_score_gemma":0.02014486,"domain_scores_codex":[0.9989513,0.0003390814,0.00004218296,0.0001699398,0.00033973,0.0001576789],"domain_scores_gemma":[0.9939031,0.003967043,0.0005810987,0.0004059595,0.0007317908,0.0004110161],"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":[0.0006013138,0.0005947897,0.005484957,0.0002948885,0.0000495405,0.000165557,0.0001832846,0.8714944,0.004689383,0.004864682,0.006475389,0.1051018],"study_design_scores_gemma":[0.00008360243,0.0005053906,0.004675977,0.00002418903,0.00003023788,0.00002861058,0.0001156645,0.9865856,0.003305543,0.001678422,0.002940746,0.00002607314],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8976468,0.001023908,0.08010107,0.0006445777,0.0001322258,0.0003374625,0.00195334,0.00409522,0.01406546],"genre_scores_gemma":[0.942448,0.0001546018,0.05440167,0.00002125753,0.00001361285,0.0001001942,0.0009223908,0.0001699583,0.001768336],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03002119,"threshold_uncertainty_score":0.05969292,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05053344543311521,"score_gpt":0.3392960141279395,"score_spread":0.2887625686948243,"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."}}