Simple regional analyses are still possible once correlated errors are removed
Bibliographic record
Abstract
Correlated errors in the monthly spherical harmonic coefficient (SHC) solutions provided by the GRACE data centers are estimated and removed using the destriping method of Crowley and Huang (2020). Regional estimates for mass change are calculated across Canada using the simple basin average technique of Swenson and Wahr (2002) as well as a simple mascon approach developed by the Canadian Geodetic Survey. A comparison with mascon solutions from the GRACE data centers demonstrates excellent agreement and in some cases reveals larger amplitudes and added temporal structure. This approach does not require additional constraints/dependencies, smoothing, normalizations or scaling factors and can easily be applied to any regional geometry without the need to calculate a global solution. Solutions tend to agree well when data quality is good and diverge when errors are larger. This is expected and demonstrates the underlying uncertainties that remain. The similarity in solutions using such different methodologies provides confidence in the time series solutions. We conclude with a regional validation that uses water level changes in the Great Lakes of North America to demonstrate the effectiveness of the method. The Great Lakes are large enough that GRACE clearly detects changes in their water levels. At the same time, the lakes are close enough to each other that distinguishing signals between adjacent lakes remains a challenge for any method. References: Crowley, J.W., and J Huang, A least-squares method for estimating the correlated error of GRACE models, Geophysical Journal International, Volume 221, Issue 3, June 2020, Pages 1736–1749, https://doi.org/10.1093/gji/ggaa104. Swenson, S., and J. Wahr, Methods for inferring regional surface-mass anomalies from Gravity Recovery and Climate Experiment (GRACE) measurements of time-variable gravity, J. Geophys. Res., 107(B9), 2193, doi:10.1029/2001JB000576, 2002.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.009 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".