New standards for reducing gravity data: The North American gravity database
Bibliographic record
Abstract
Abstract The North American gravity database as well as data-bases from Canada, Mexico, and the United States are being revised to improve their coverage, versatility, and accuracy. An important part of this effort is revising procedures for calculating gravity anomalies, taking into account our enhanced computational power, improved terrain databases and datums, and increased interest in more accurately defining long-wavelength anomaly components. Users of the databases may note minor differences between previous and revised database values as a result of these procedures. Generally, the differences do not impact the interpretation of local anomalies but do improve regional anomaly studies. The most striking revision is the use of the internationally accepted terrestrial ellipsoid for the height datum of gravity stations rather than the conventionally used geoid or sea level. Principal facts of gravity observations and anomalies based on both revised and previous procedures together with germane metadata will be available on an interactive Web-based data system as well as from national agencies and data centers. The use of the revised procedures is encouraged for gravity data reduction because of the widespread use of the global positioning system in gravity fieldwork and the need for increased accuracy and precision of anomalies and consistency with North American and national databases. Anomalies based on the revised standards should be preceded by the adjective “ellipsoidal” to differentiate anomalies calculated using heights with respect to the ellipsoid from those based on conventional elevations referenced to the geoid.
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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 | 0.019 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.018 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".