Continental mass change from GRACE over 2002-2011 and its impact on sea level
Bibliographic record
Abstract
Present-day continental mass variation as observed by space gravimetry reveals secular mass decline and accumulation. Whereas the former contributes to sea-level rise, the latter results in sea-level fall. As such, consideration of mass accumulation (rather than focussing solely on mass loss) is important for reliable overall estimates of sea-level change. Using data from the Gravity Recovery And Climate Experiment satellite mission, we quantify mass-change trends in 19 continental areas that exhibit a dominant signal. The integrated mass change within these regions is representative of the variation over the whole land areas. During the integer 9-year period of May 2002 to April 2011, GIA-adjusted mass gain and mass loss in these areas contributed, on average, to −(0.7 ± 0.4) mm/year of sea-level fall and + (1.8 ± 0.2) mm/year of sea-level rise; the net effect was + (1.1 ± 0.6) mm/year. Ice melting over Greenland, Iceland, Svalbard, the Canadian Arctic archipelago, Antarctica, Alaska and Patagonia was responsible for + (1.4±0.2) mm/year of the total balance. Hence, land-water mass accumulation compensated about 20 % of the impact of ice-melt water influx to the oceans. In order to assess the impact of geocentre motion, we converted geocentre coordinates derived from satellite laser ranging (SLR) to degree-one geopotential coefficients. We found geocentre motion to introduce small biases to mass-change and sea-level change estimates; its overall effect is + (0.1 ± 0.1) mm/year. This value, however, should be taken with care owing to questionable reliability of secular trends in SLR-derived geocentre coordinates.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".