{"id":"W3215501295","doi":"10.1109/tii.2021.3129825","title":"The TriLS Approach for Drift-Aware Time-Series Prediction in IIoT Environment","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Informatics","topic":"Data Stream Mining Techniques","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Cloud computing; Computer science; Gateway (web page); Overhead (engineering); Automation; Default gateway; Real-time computing; Time series; Distributed computing; The Internet; Data mining; Reliability engineering; Machine learning; Computer network; Engineering; Operating system","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":[0.000507837,0.0001639632,0.0001864654,0.0001193618,0.0003380507,0.0003382007,0.0005674407,0.0002141713,0.00001511588],"category_scores_gemma":[0.00002856522,0.0001364982,0.00008770652,0.0003483602,0.00007450382,0.0009621777,0.000010926,0.0003936394,0.00002309381],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000153454,"about_ca_system_score_gemma":0.0001660368,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006868169,"about_ca_topic_score_gemma":0.000002954702,"domain_scores_codex":[0.998588,0.00006790792,0.000606456,0.0001677662,0.0002915413,0.0002782973],"domain_scores_gemma":[0.9988184,0.000248285,0.0001424436,0.0006852428,0.00004258271,0.00006302778],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004107138,0.001185648,0.00004090189,0.00009501716,0.0002695241,0.000008001212,0.006496356,0.08338429,0.0003219825,0.005270977,0.06650805,0.8360085],"study_design_scores_gemma":[0.003592563,0.001077864,0.00001891664,0.0001384667,0.00007307556,0.00007331414,0.001900434,0.741625,0.1204986,0.0009334615,0.1294045,0.000663847],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0004777683,0.000005963363,0.9969137,0.0003562712,0.0004675052,0.0005771354,0.0002245796,0.0001967264,0.0007803813],"genre_scores_gemma":[0.1432444,0.0003778014,0.8440448,0.0005545639,0.0005244555,0.002547789,0.0004112756,0.00008414715,0.008210748],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8353447,"threshold_uncertainty_score":0.5566233,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.037288924520489,"score_gpt":0.2370165796252527,"score_spread":0.1997276551047638,"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."}}