{"id":"W2907210592","doi":"10.3390/make1010018","title":"Evaluation of ARIMA Models for Human–Machine Interface State Sequence Prediction","year":2019,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Human-Automation Interaction and Safety","field":"Psychology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Power Generation; Ontario Tech University","funders":"","keywords":"Autoregressive integrated moving average; Interface (matter); Computer science; Situation awareness; Time series; Process (computing); Sequence (biology); Autoregressive model; Human–machine interface; Human–machine system; Data mining; Operator (biology); Human error; State (computer science); Series (stratigraphy); Artificial intelligence; Machine learning; Engineering; Econometrics; Algorithm; Reliability engineering; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002144648,0.0001673586,0.0002204961,0.0002274531,0.0002023077,0.00003477028,0.00006683415,0.0001130339,0.00186139],"category_scores_gemma":[0.0001158963,0.000165994,0.00007935789,0.0001184516,0.00003629385,0.0003512647,0.00002266886,0.0004066009,0.0001566694],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001363616,"about_ca_system_score_gemma":0.00004341264,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002492798,"about_ca_topic_score_gemma":0.0001371547,"domain_scores_codex":[0.9981942,0.0005392339,0.0004612237,0.0003658942,0.0002636531,0.0001758426],"domain_scores_gemma":[0.9986888,0.0001913363,0.0003491,0.0001725429,0.0005433278,0.00005495187],"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":[0.00116817,0.001222908,0.02109741,0.0004414532,0.0006038878,0.000001146451,0.02827792,0.08616661,0.09602397,0.01468137,0.001341487,0.7489737],"study_design_scores_gemma":[0.002311868,0.0005007455,0.009899241,0.00007814509,0.0001320022,0.00003289226,0.0006208841,0.965008,0.0007827891,0.002549131,0.0178943,0.0001900084],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8319785,0.001376546,0.08176219,0.0001049398,0.002209904,0.001007289,0.00006408758,0.0002738729,0.08122264],"genre_scores_gemma":[0.9833192,0.0000254231,0.0001509876,0.000009368591,0.00009332525,0.00008676326,0.0001359455,0.00002858958,0.01615045],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8788414,"threshold_uncertainty_score":0.999051,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07389168470611468,"score_gpt":0.4390646777614761,"score_spread":0.3651729930553614,"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."}}