Toward 1-mGal accuracy in global marine gravity from CryoSat-2, Envisat, and Jason-1
Bibliographic record
Abstract
More than 60% of the Earth's land and shallow marine areas are covered by > 2 km of sediments and sedimentary rocks, with the thickest accumulations on rifted continental margins (Figure 1). Free-air marine gravity anomalies derived from Geosat and ERS-1 satellite altimetry (Fairhead et al., 2001; Sandwell and Smith, 2009; Andersen et al., 2009) outline most of these major basins with remarkable precision. Moreover, gravity and bathymetry data derived from altimetry are used to identify current and paleo-submarine canyons, faults, and local recent uplifts. These geomorphic features provide clues to where to look for large deposits of sediments. While current altimeter data delineate large offshore basins and major structures, they do not resolve some of the smaller geomorphic features and basins (Yale et al., 1998; Fairhead et al., 2001). Improved accuracy and resolution is desirable: to facilitate comparisons between continental margins; as an exploration tool and to permit extrapolation of known structures from well-surveyed areas; to follow fracture zones out of the deep-ocean basin into antecedent continental structures, to define and compare segmentation of margins along strike and identify the position of the continent-ocean boundary; and to study mass anomalies (e.g., sediment type and distribution) and isostatic compensation at continental margins. In this article, we assess the accuracy of a new global marine gravity model based on a wealth of new radar altimetry data and demonstrate that these gravity data are superior in quality to the majority of publicly available academic and government ship gravity data.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.004 |
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".