{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002752404,0.0001652853,0.0001905413,0.0001965119,0.00003594228,0.0001143613,0.0005886576,0.00006953398,0.00000856791],"category_scores_gemma":[0.0003666236,0.0001556499,0.00008597223,0.0004415984,0.00001271565,0.0005115831,0.0004272917,0.0003399092,0.00001341561],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007159151,"about_ca_system_score_gemma":0.00003670202,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003709805,"about_ca_topic_score_gemma":8.589474e-7,"domain_scores_codex":[0.9988047,0.00002198482,0.0002780303,0.0003665997,0.000262823,0.0002658519],"domain_scores_gemma":[0.9990882,0.0003934262,0.00003123344,0.0003566993,0.00004014873,0.00009031084],"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":[0.00002200077,0.0002858817,0.02683925,0.001505411,0.0004046397,0.0006675712,0.006248757,0.485136,0.01222318,0.02891291,0.005304981,0.4324495],"study_design_scores_gemma":[0.00005493546,0.0001029057,0.0004159314,0.0002818532,0.000008594384,0.00002696031,0.00000276692,0.9836341,0.00539412,0.0007637047,0.009080454,0.0002336677],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0205223,0.001087032,0.9744176,0.0002284208,0.0002391115,0.00007262552,0.00001127076,0.003364155,0.00005744647],"genre_scores_gemma":[0.5782191,0.00002720494,0.4215511,0.00004522328,0.00004492578,0.00001270142,0.00001175191,0.00003154784,0.00005640254],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5576968,"threshold_uncertainty_score":0.6347218,"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."}}