{"id":"W4230500706","doi":"10.1002/spip.374","title":"Optimized mismatch resolution for COTS selection","year":2008,"lang":"en","type":"article","venue":"Software Process Improvement and Practice","topic":"Embedded Systems Design Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Process (computing); Selection (genetic algorithm); Reliability engineering; Software; Systems engineering; Computer science; Resource (disambiguation); Domain (mathematical analysis); Product (mathematics); Risk analysis (engineering); Engineering; Software engineering; Artificial intelligence; Operating 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":[0.002561613,0.001036582,0.0008368192,0.001489179,0.0006320899,0.001019792,0.00100562,0.0007553981,0.002982717],"category_scores_gemma":[0.0109036,0.0003862241,0.0005758393,0.001334781,0.0004089387,0.001045586,0.001542496,0.0007104892,0.0006164099],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005211703,"about_ca_system_score_gemma":0.0009047208,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001241447,"about_ca_topic_score_gemma":0.001416155,"domain_scores_codex":[0.9966263,0.001209396,0.0002282936,0.0004698235,0.001258951,0.0002072031],"domain_scores_gemma":[0.9935524,0.003302156,0.0008971491,0.0009239907,0.001146832,0.0001773612],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005631197,0.0002209733,0.009528526,0.0003014707,0.0001097026,0.0006871313,0.0006441969,0.2312968,0.03965577,0.01517111,0.003997807,0.6978233],"study_design_scores_gemma":[0.00004844279,0.000284395,0.002190011,0.0000366206,0.0000701339,0.0006152646,0.0002576838,0.9533056,0.02637493,0.01054441,0.006234324,0.00003815544],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08479643,0.0003872697,0.9092355,0.0001460848,0.00005629785,0.0001810819,0.00008928643,0.001652098,0.003455834],"genre_scores_gemma":[0.4892726,0.0000965799,0.5076703,0.00009781681,0.00002366756,0.0001227382,0.0002096496,0.0001700332,0.002336518],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002982717,"threshold_uncertainty_score":0.01354724,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03436764899921617,"score_gpt":0.3129931203933215,"score_spread":0.2786254713941053,"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."}}