{"id":"W3157784407","doi":"10.18280/mmep.080211","title":"A Nonstationary Mathematical Model for Acceleration Time Series","year":2021,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Acceleration; Series (stratigraphy); Autoregressive model; Displacement (psychology); Nonlinear system; Time series; Autoregressive–moving-average model; Moving-average model; Autocorrelation; Mathematics; Applied mathematics; Computer science; Autoregressive integrated moving average; Econometrics; Statistics; Physics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001539521,0.0002050096,0.0002710976,0.00005930113,0.00007689268,0.00007688395,0.00006959013,0.0001244424,0.0000157954],"category_scores_gemma":[0.00004773827,0.0002037346,0.00005145334,0.00009216972,0.00001943759,0.0001895695,0.00002675309,0.0001516105,0.00001193738],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004601008,"about_ca_system_score_gemma":0.00001813669,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":2.460842e-7,"about_ca_topic_score_gemma":6.366841e-8,"domain_scores_codex":[0.9989899,0.000005622467,0.0003614589,0.00019978,0.0001383576,0.0003048708],"domain_scores_gemma":[0.9994571,0.000160629,0.00001965677,0.0001752795,0.00007492588,0.0001124725],"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":[0.0000027886,0.00001180958,4.860506e-7,0.001876186,0.00001730099,0.000001229922,0.0004762874,0.9702227,0.001371741,0.02523028,0.0000933039,0.0006958604],"study_design_scores_gemma":[0.00009954915,0.00001545124,0.000001452246,0.0002326985,0.00001554606,0.00003959788,0.000007884356,0.8469376,0.001612739,0.1507771,0.00007024748,0.0001901421],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0302989,0.0002168587,0.9677396,0.0001052258,0.00005402656,0.0002980155,0.00001252058,0.0009422827,0.0003325936],"genre_scores_gemma":[0.296521,0.00008651211,0.7026018,0.0000102158,0.00007768046,0.0002379658,0.00002141377,0.00007321956,0.0003702143],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2662221,"threshold_uncertainty_score":0.8308055,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03575273977405619,"score_gpt":0.2503327216007484,"score_spread":0.2145799818266922,"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."}}