Resolving the discrepancy betweenthe seasonal oscillation of Earth's fluid envelope estimated with SLR and that assumed in GRACE
Bibliographic record
Abstract
For 25 years, geodesists have inferred that the displacement of the "geocenter" estimated from (SLR) satellite laser ranging represents fluctuation of Earth's fluid envelope relative to solid Earth.  However, SLR determines the displacement of the (CN) center of network of geodetic sites relative to the (CM) center of mass of Earth, consisting of solid Earth, the oceans, the atmosphere, and continental water, snow, and ice. Because solid Earth's surface is deforming in elastic response to the changing load of continental water, atmosphere and oceans, CN only roughly approximates the (CE) center of mass of solid Earth.  In this study, estimate the velocity of CM relative to the (CE) center of mass of Earth by first correcting SLR site displacements (estimated by the International Laser Ranging Service 2020) for their elastic response relative to CE produced by fluctuations of continental water, atmosphere and oceans.  We maintain that by correcting for loading displacements relative to CE, we arrive at an estimate of the displacement of CE.  We find that transforming the SLR series from CN to CE reduces the discrepancy between the seasonal oscillation of Earth's fluid envelope estimated by SLR and that assumed by GRACE (using the technique of Sun et al. 2017) by 40 per cent.  In both SLR and GRACE, a total of 0.5 x 1016 kg of mass moves between hemispheres from southern oceans in August to snow-covered areas in North America and Europe (in particular in Canada and Siberia).  The primary remaining difference between the two techniques is that mass in the northern hemisphere is maximum on February 5 in SLR, 20 days before it is maximum on Feb 25 in GRACE.  Knowing the total transfer of the mass of between hemispheres places a boundary constraint on global models of circulation of water on land and in the oceans and atmospheres (that may be applied to forecasting extreme events such as flooding and drought).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".