{"id":"W4220908806","doi":"10.18280/ria.360109","title":"Novel Optimized Reusable Component Repository Using Neural Networks","year":2022,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Software Engineering Research","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Component (thermodynamics); Computer science; Software; Information repository; Artificial neural network; Work (physics); Component-based software engineering; Data science; World Wide Web; Software engineering; Database; Software development; Computer data storage; Engineering; Artificial intelligence; Operating system","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005929049,0.0007454228,0.001105459,0.00140263,0.0005609425,0.001148867,0.002118077,0.0009778744,0.002781642],"category_scores_gemma":[0.001298741,0.0004797495,0.00100299,0.001385956,0.0002699706,0.001677996,0.0008411771,0.000645049,0.0007759265],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001014384,"about_ca_system_score_gemma":0.001057889,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01154698,"about_ca_topic_score_gemma":0.00961524,"domain_scores_codex":[0.9996428,0.00004378053,0.00002937322,0.0001050048,0.0001021196,0.00007692558],"domain_scores_gemma":[0.9995592,0.0001145778,0.0000752761,0.00005760834,0.0001659452,0.00002724123],"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":[0.0002967879,0.0002419087,0.001774707,0.0001480624,0.0001386819,0.0002240609,0.00006043235,0.5235929,0.008984061,0.004663372,0.004739068,0.455136],"study_design_scores_gemma":[0.00001202579,0.00004671039,0.0002030447,0.000008695524,0.00002430404,0.00004339333,0.000009602872,0.9958434,0.002198606,0.0008688401,0.0007338336,0.000007537718],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09397286,0.001437474,0.8907556,0.0003349119,0.0001781172,0.0001526739,0.0002310444,0.006033353,0.006903911],"genre_scores_gemma":[0.7636927,0.0007252447,0.222276,0.0001567783,0.0000684821,0.0002535212,0.0006191243,0.0001637073,0.01204446],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01154698,"threshold_uncertainty_score":0.02295953,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05333537265837602,"score_gpt":0.277295462772953,"score_spread":0.223960090114577,"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."}}