Water Storage Changes in Three Gorges Water Systems Area Inferred from Grace Time‐Variable Gravity Data
Bibliographic record
Abstract
Abstract Twenty‐two monthly water storage changes are predicted for the supply water systems of the Three Gorges reservoir from GRACE time‐variable gravity data. In order to assess the results, the CPC hydrological models are used to establish two benchmarks. It is found that the results are very reasonable in this area. For Gaussian averaging for 1000 km radius, the total water storage changes in the area have a peak‐to‐peak value of 14 cm, and the annual component has an amplitude of 5.8 cm and a phase of –40.8 days. The RMS difference compared with the inversion results with the same averaging radius using the synthetic gravity data from the CPC models is 1.3 cm for the total water storage changes, and the differences are 0.1 cm and 1.0 day for the amplitude and phase of the annual component. However, for checking the ability of GRACE to monitor the true water storage changes within the area, it is also necessary to compare the inversion results from GRACE gravity models with the true average results of CPC models. For this comparison the RMS difference is 2.1 cm for the total water storage changes, and the differences are 1.7 cm and 9.3 days for the amplitude and phase of the annual component. Consequently, it is found that the first comparison has overestimated the effectiveness of GRACE. Nevertheless, the second comparison shows that the monthly water storage changes can be roughly determined from GRACE data in this area.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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 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".