{"id":"W3133826961","doi":"10.1007/s10270-020-00856-9","title":"Predictions-on-chip: model-based training and automated deployment of machine learning models at runtime","year":2021,"lang":"en","type":"article","venue":"Software & Systems Modeling","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"Siemens (Canada); McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Software deployment; Context (archaeology); Artificial neural network; Process (computing); Artificial intelligence; Machine learning; Software 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":[0.001026739,0.002084952,0.00104476,0.0005394336,0.0003874447,0.001502802,0.003349933,0.0009921966,0.008804373],"category_scores_gemma":[0.006793078,0.001303536,0.0008847728,0.000553953,0.000582629,0.002562584,0.001858337,0.002732008,0.003660582],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008706457,"about_ca_system_score_gemma":0.002196181,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009620072,"about_ca_topic_score_gemma":0.01480004,"domain_scores_codex":[0.9992835,0.0001757407,0.00003785274,0.0002357018,0.0001747763,0.00009245147],"domain_scores_gemma":[0.9965139,0.001826875,0.0001769592,0.0009535048,0.0003754684,0.0001531849],"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":[0.001848481,0.0009514927,0.008256087,0.0003261907,0.0003237249,0.0005750004,0.0003170712,0.5591962,0.02173265,0.006479032,0.04770643,0.3522877],"study_design_scores_gemma":[0.0000303708,0.00003864815,0.0002229977,0.000006619452,0.00001208923,0.00001555133,0.00001384074,0.9925938,0.004411813,0.001686195,0.0009577468,0.00001032951],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.09323823,0.0006707028,0.7267326,0.0006661571,0.0004428887,0.0001627779,0.00225999,0.1706791,0.005147636],"genre_scores_gemma":[0.7320191,0.0003258232,0.252026,0.000506901,0.00007174327,0.0002049385,0.005206017,0.005928823,0.003710615],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009620072,"threshold_uncertainty_score":0.02945358,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02399034816881664,"score_gpt":0.220199544127288,"score_spread":0.1962091959584713,"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."}}