Bibliographic record
Abstract
A method is described for mapping time-uncorrelated large-scale errors in satellite altimeter sea surface heights. Standard deviations of differences between pairs of successive measurements at track crossovers are computed, and the functional dependence of these deviations on absolute time difference is used to estimate the errors of individual measurements. This is first applied to all of ERS-1,2 altimeter data in the Pacific Ocean, yielding average errors of 3.2 cm in the deep ocean (>1 km) and 4.7 cm in the shallow seas (<1 km). The procedure is repeated for variable latitude bands, each with a full range of possible time differences, yielding a meridional profile of computed errors, ranging from 2.6 cm near the Antarctic continent (67–60S) and South Subtropical regions (25–5S) to 3.5 cm in the Antarctic Circumpolar Current (60–45S) and the Northern Hemisphere Subtropical and Subpolar Gyres. Finally, coarse-resolution maps of these errors are produced by subdividing the Pacific Ocean into latitude-longitude bins, each large enough to contain a sufficient number of samples for the functional fits. The larger errors are in Northwest and Subtropical Pacific, especially in South China Sea (4.3 to 4.5 cm) and off northern Australia (5.4 cm), while the smaller errors (2.5 to 3 cm) are in Northeast Pacific, central Tropical Pacific and near Antarctica in Southeast Pacific Ocean. These are lower bounds on altimeter errors, as they do not include contributions from time-correlated errors. We find that the computed error fields are not correlated with sea level standard deviations, thus disproving the notion that altimeter error variance can be scaled with the variance of sea surface height data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.005 | 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".