{"id":"W4248066596","doi":"10.5194/bgd-9-18175-2012","title":"Automated quality control methods for sensor data: a novel observatory approach","year":2012,"lang":"en","type":"preprint","venue":"","topic":"Data Analysis with R","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"U.S. Geological Survey; National Oceanic and Atmospheric Administration; Canadian Foundation for Climate and Atmospheric Sciences; Oklahoma State University; U.S. Department of the Interior; University of Oklahoma; U.S. Department of Agriculture; U.S. Department of Energy; National Science Foundation","keywords":"Suite; Computer science; Quality assurance; Data quality; Data mining; Interoperability; Quality (philosophy); Real-time computing; Engineering; Geography; Operating system","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02143951,0.001365946,0.001455389,0.004475526,0.001065872,0.004974293,0.003514017,0.001524485,0.001809307],"category_scores_gemma":[0.09707289,0.0008318139,0.001487539,0.003366648,0.002336812,0.004262465,0.004528806,0.003231871,0.0007300058],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001781586,"about_ca_system_score_gemma":0.003303838,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003713073,"about_ca_topic_score_gemma":0.002695494,"domain_scores_codex":[0.9698642,0.01141436,0.002402629,0.004692193,0.0111376,0.0004889858],"domain_scores_gemma":[0.9014137,0.0487674,0.01208921,0.02135856,0.01548045,0.0008906905],"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":[0.00050954,0.0005906654,0.03277649,0.0005655282,0.0005704314,0.0003812961,0.00111997,0.1054327,0.03086235,0.09112265,0.007889644,0.7281787],"study_design_scores_gemma":[0.0001185289,0.0002155814,0.0073344,0.00007600251,0.00007642342,0.0002776808,0.000163968,0.8894982,0.01906133,0.07453828,0.008523929,0.0001158545],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002820504,0.00006562912,0.9951916,0.0001303954,0.00002170002,0.00009236955,0.00007870187,0.001283879,0.0003153206],"genre_scores_gemma":[0.1092789,0.0000721703,0.8889316,0.0001351434,0.0000880422,0.0003198739,0.0004080551,0.0003280925,0.0004380177],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02143951,"threshold_uncertainty_score":0.1133843,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2838216642023632,"score_gpt":0.4550077184919297,"score_spread":0.1711860542895665,"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."}}