{"id":"W3187034484","doi":"10.3390/engproc2021005057","title":"Assessing Statistical Performance of Time Series Interpolators","year":2021,"lang":"en","type":"article","venue":"","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Trent University","funders":"","keywords":"Series (stratigraphy); Interpolation (computer graphics); Computer science; Missing data; Time series; Sampling (signal processing); Noise (video); Algorithm; Data mining; Polynomial; Artificial intelligence; Machine learning; Mathematics; Computer vision; Filter (signal processing)","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":[],"consensus_categories":[],"category_scores_codex":[0.0001204878,0.00006131754,0.0001453219,0.00003155963,0.00006070087,0.0001691387,0.0001930725,0.00001986094,0.0004851585],"category_scores_gemma":[0.00003944984,0.00005126094,0.0000385399,0.0002815657,0.00004102682,0.0009390927,0.0002144711,0.00005031751,0.00003619807],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000948181,"about_ca_system_score_gemma":0.00006055806,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007661208,"about_ca_topic_score_gemma":0.000002777837,"domain_scores_codex":[0.9993429,0.00002513804,0.0001994357,0.0001651948,0.0001364642,0.0001308814],"domain_scores_gemma":[0.9995295,0.00004773213,0.00005754322,0.0002271671,0.00009811932,0.00003996411],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001740304,0.0001890174,0.03210116,0.0001716474,0.0001965405,0.00009762739,0.001186901,0.0006677493,0.03673308,0.4644286,0.001599505,0.4626107],"study_design_scores_gemma":[0.00009374804,0.0001015513,0.01089534,0.00004765616,0.00001393664,0.00006128537,0.0001966691,0.9491491,0.03737854,0.0005629228,0.001316001,0.0001832627],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.333633,0.00003519879,0.6460968,0.0001639,0.00008163925,0.00001526887,0.000001419506,0.0000569147,0.01991584],"genre_scores_gemma":[0.763215,0.000004822599,0.2353406,0.00003366196,0.00001363109,4.205957e-7,0.000002467136,0.000002971318,0.001386483],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9484813,"threshold_uncertainty_score":0.5312145,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0118037591887602,"score_gpt":0.2420530307216611,"score_spread":0.2302492715329009,"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."}}