{"id":"W4399703649","doi":"10.1007/s10664-024-10492-2","title":"Post deployment recycling of machine learning models","year":2024,"lang":"en","type":"article","venue":"Empirical Software Engineering","topic":"Data Stream Mining Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Software deployment; Reuse; Computer science; Artificial intelligence; Machine learning; Baseline (sea); Artificial neural network; Inference; Random forest; Logistic regression; Predictive modelling; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01010277,0.001489813,0.00149811,0.00350416,0.001362423,0.004911464,0.00313324,0.001902161,0.01004413],"category_scores_gemma":[0.104782,0.001696813,0.001711015,0.002273978,0.001713729,0.005971855,0.004584131,0.003760066,0.006187413],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0015119,"about_ca_system_score_gemma":0.003668542,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008925815,"about_ca_topic_score_gemma":0.0116597,"domain_scores_codex":[0.9896519,0.002418686,0.0007781363,0.001151959,0.004920952,0.001078311],"domain_scores_gemma":[0.8854235,0.03272832,0.002693688,0.05773474,0.02001512,0.001404573],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00156585,0.001805542,0.03776989,0.0008008796,0.0003827734,0.00140266,0.003277368,0.0428395,0.03048959,0.02373489,0.04573828,0.8101928],"study_design_scores_gemma":[0.0001577224,0.001193434,0.03809616,0.0005102161,0.0005022776,0.001899807,0.002242295,0.7388988,0.09912766,0.04033003,0.07684005,0.000201551],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.620885,0.001548519,0.2914445,0.006705656,0.001450981,0.0007016083,0.002442363,0.04900776,0.02581355],"genre_scores_gemma":[0.8229087,0.0005450645,0.1387157,0.0009077172,0.0002041671,0.0002198805,0.004343373,0.006294884,0.02586046],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01010277,"threshold_uncertainty_score":0.05342919,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03166989974971166,"score_gpt":0.2737139312476823,"score_spread":0.2420440314979707,"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."}}