New gravity data in the Arctic Ocean: Comparison of airborne and ERS gravity
Bibliographic record
Abstract
New gravity fields from airborne gravimetry and from ERS‐1 and ‐2 satellite altimetry cover extensive portions of the Arctic Ocean. These two data sets may constitute as much as 60% of the data contributions to the Arctic Gravity Project compilation. Here we evaluate the accuracy and resolution of these data and quantify their impact on the compilation. Both gravity determinations compare favorably with Geological Survey of Canada surface measurements in the Beaufort Sea (airborne, 1.86–2.09 mGal rms; ERS, 2.64–3.11 mGal rms). Comparisons between the airborne and ERS data over the Chukchi Borderlands reveal a 4.38 mGal rms difference over the smoother region of the field and 7.36 mGal rms over the rugose field generated by the shallow ridges and deep troughs. Coherency between the two data sets in the Chukchi region implies a resolution of 19 km. Comparison with Science Ice Expedition submarine measurements over Chukchi Plateau suggests that the ERS field resolves even shorter‐wavelength signal than the airborne data, whereas in the Beaufort Sea the airborne data showed better coherence to ground truth data. Long‐wavelength differences exist between the two data sets, expressed as a 2–3 mGal offset over the Chukchi region. This study highlights the respective strengths of the two data sets. The ERS gravity field has the advantage of ubiquitous coverage of the ocean south of 81.5°N, a denser sampling of the gravity field, and a recovery of signal down to ∼15 km. The airborne data cover a significant portion of the polar hole in the satellite coverage, have lower measurement noise, and recover somewhat higher anomaly amplitudes in the 25–100 km wavelength range.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.000 | 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 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".