{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003285209,0.0003067566,0.0005463645,0.0001857544,0.0002365611,0.00006819483,0.00009655856,0.0001752966,0.000006163703],"category_scores_gemma":[0.00005929287,0.0003201419,0.0001280204,0.0002177395,0.00002015964,0.0001435336,0.00003810054,0.0002878214,0.000006477822],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002000393,"about_ca_system_score_gemma":0.00007083682,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001086114,"about_ca_topic_score_gemma":0.00002761582,"domain_scores_codex":[0.9980523,0.0001116353,0.000695307,0.0003875766,0.0003900275,0.0003631615],"domain_scores_gemma":[0.9992,0.00009937348,0.0001013263,0.0003098691,0.0001365162,0.0001529455],"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":[0.00001978827,0.00001848974,0.000160205,0.0003284759,0.000100918,0.00000739808,0.0006454992,0.9954858,0.002581546,0.00005740579,0.00002784902,0.0005666623],"study_design_scores_gemma":[0.0009074861,0.00004650119,0.000002640099,0.0004845251,0.00004900317,0.00004170159,0.0003586372,0.9974132,0.0002887363,0.00003537146,0.00009342792,0.0002787716],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2568497,0.003300807,0.7358869,0.00001127307,0.0004863452,0.0002868592,0.00005019989,0.002808575,0.0003193588],"genre_scores_gemma":[0.99793,0.00004192303,0.001464059,0.00002018699,0.00006966303,0.0001084384,0.0000413249,0.0001070281,0.0002173954],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7410803,"threshold_uncertainty_score":0.9999251,"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."}}