{"id":"W7065017813","doi":"","title":"Detecting and Imputing Hidden Missing Values in Time Series Data : Case study: Alfa Laval","year":2024,"lang":"en","type":"article","venue":"Hogskolan Ihalmstad (Halmstad University)","topic":"Astrophysical Phenomena and Observations","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Missing data; Imputation (statistics); Time series; Series (stratigraphy); Interpolation (computer graphics); Identification (biology)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002649127,0.0002661034,0.0002938937,0.0002975132,0.000436572,0.0003156886,0.0003955233,0.00004561404,0.00006442478],"category_scores_gemma":[0.00001395032,0.0002884535,0.0000586603,0.0007462147,0.00009707603,0.001134258,0.0007378277,0.000375826,0.0000237614],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007564812,"about_ca_system_score_gemma":0.0001261062,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002076245,"about_ca_topic_score_gemma":0.0001218763,"domain_scores_codex":[0.9983218,0.0001521208,0.0002406671,0.0006800175,0.0001888436,0.0004165501],"domain_scores_gemma":[0.9990315,0.000208924,0.00007132149,0.0004923954,0.0000410054,0.0001547853],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"qualitative","study_design_scores_codex":[0.0002844455,0.001742638,0.5763035,0.0003847432,0.001391726,0.03251586,0.04308259,0.0007424051,0.006547128,0.02478912,0.001074153,0.3111416],"study_design_scores_gemma":[0.01213402,0.002672718,0.06530736,0.002206328,0.002793089,0.0009073649,0.6184251,0.2168293,0.001438769,0.02343607,0.04621377,0.007636165],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9942131,0.00009570166,0.001191964,0.0001340235,0.00009230903,0.0002832659,0.0001167849,0.0001423181,0.003730544],"genre_scores_gemma":[0.996987,0.000002933029,0.001006077,0.000006346644,0.0002707425,9.282188e-7,0.0000609638,0.00003554826,0.001629466],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5753425,"threshold_uncertainty_score":0.9999568,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02105125876847465,"score_gpt":0.2420539003841781,"score_spread":0.2210026416157035,"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."}}