Results from Global Cross Calibration and Validation of Jason-2 and Jason-3
Bibliographic record
Abstract
We present results from a global comparison of the Jason-2 (J2) and Jason-3 (J3) measurement systems during their formation-flying phase. Our analysis leverages the identical oceanographic and environmental conditions that are effectively being observed by the two systems during this phase, since they are flying along the same ground track but only 82 seconds apart. We characterize both geographically correlated and systematic differences between the two measurement systems, with the goal of facilitating a seamless transition of the reference altimeter mission from J2 to J3. Our study emphasizes the evaluation of orbit solutions for each mission and associated geographically-correlated errors, relative Ku- and C-band altimeter range biases, potential relative altimeter tracker bias, and relative calibration bias in the radiometer measurements of wet troposphere delay. We also inter-compare overall data noise from each measurement system using the typical metrics of sea surface height (SSH) differences, as well as SSH cross-over differences. Early results from IGDR-D data reveal a J3-J2 inter-satellite SSH bias of approximately -3 cm (J3 measuring lower than J2). This bias is primarily due to a relative Ku-band range bias of +2.1 cm (J3 measuring longer than J2), and Ku-band ionosphere correction bias of +0.5 cm. The latter arises from the confluence of the relative Ku-band range bias and an observed relative C-band range bias of -0.9 cm. The remaining contributor to the observed relative SSH bias is a relative drift in the radiometer wet troposphere correction that is expected to be mitigated with an improved calibration to the J3 radiometer. We also observe a systematic +/- 2 cm east/west pattern in the J3-J2 IGDR-D SSH differences that is eliminated when using orbit solutions based upon GPS tracking 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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".