{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009664953,0.001295855,0.001043651,0.001216814,0.0005639801,0.001158284,0.001525968,0.00109384,0.001874074],"category_scores_gemma":[0.02425904,0.0004372195,0.0008515214,0.0008375527,0.0002640321,0.001661943,0.00067177,0.001948098,0.0006177436],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009679298,"about_ca_system_score_gemma":0.00156381,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03230719,"about_ca_topic_score_gemma":0.0194483,"domain_scores_codex":[0.9976412,0.001187404,0.0001920624,0.0004379244,0.000407339,0.0001341428],"domain_scores_gemma":[0.9833524,0.01295518,0.0005642354,0.0005535654,0.002355818,0.0002187151],"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":[0.00146378,0.0005068589,0.01204484,0.0002058604,0.0004384169,0.00007668922,0.0001725718,0.7943064,0.002880248,0.002466566,0.001403056,0.1840346],"study_design_scores_gemma":[0.000008705556,0.00008065715,0.0007898723,0.000006271023,0.0000163937,0.000006831069,0.00001649065,0.9981815,0.0004575157,0.0002878201,0.000139209,0.000008868703],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3031641,0.00228252,0.6853108,0.0008592542,0.0003235128,0.000254885,0.0008032433,0.003632305,0.00336938],"genre_scores_gemma":[0.8845286,0.0005441071,0.1119749,0.0001266296,0.00006728304,0.0001846119,0.0008693228,0.0001210091,0.001583441],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03230719,"threshold_uncertainty_score":0.06423825,"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."}}