{"id":"W4404644187","doi":"10.1029/2024gl110588","title":"Exploring Thermospheric Disturbance Patterns Through Space‐Borne Accelerometer Measurement Errors: A Weighted Accelerometer 1B Dataset of GRACE C","year":2024,"lang":"en","type":"article","venue":"Geophysical Research Letters","topic":"Geophysics and Gravity Measurements","field":"Earth and Planetary Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Accelerometer; Disturbance (geology); Remote sensing; Environmental science; Space (punctuation); Geodesy; Thermosphere; Observational error; Meteorology; Computer science; Atmospheric sciences; Geology; Mathematics; Statistics; Geophysics; Ionosphere; Physics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0006103156,0.0005461627,0.0004378507,0.001072486,0.0003692069,0.0005179832,0.0005548939,0.0005636963,0.0008932393],"category_scores_gemma":[0.001518132,0.0001884987,0.0004865703,0.001383341,0.000362128,0.0002967219,0.0007565139,0.0006375563,0.000742116],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002882193,"about_ca_system_score_gemma":0.000611347,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02329364,"about_ca_topic_score_gemma":0.04078181,"domain_scores_codex":[0.9995621,0.00008993076,0.00003206507,0.0001090958,0.0001272425,0.00007952734],"domain_scores_gemma":[0.9989104,0.0001383114,0.000202491,0.0003044269,0.0003185174,0.0001258485],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001762588,0.001482443,0.6497154,0.0005222106,0.001119969,0.001038055,0.0008896648,0.07704274,0.05683439,0.003313257,0.1014073,0.104872],"study_design_scores_gemma":[0.0001363214,0.0001567393,0.9139698,0.00004701644,0.00006563167,0.0002045529,0.0002750083,0.06197332,0.004093837,0.0007503271,0.01823808,0.0000893934],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9303845,0.0001768975,0.005047854,0.0003620287,0.00009450628,0.00005073006,0.06114504,0.000808495,0.001929913],"genre_scores_gemma":[0.8461952,0.0001028545,0.008043353,0.0001054121,0.0000929695,0.00009694811,0.1441905,0.0001752949,0.0009975027],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02329364,"threshold_uncertainty_score":0.04631615,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2376694229262983,"score_gpt":0.3201778711518156,"score_spread":0.08250844822551737,"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."}}