Assessing the quality of GEOID12B model through field surveys
Bibliographic record
Abstract
Abstract Elevation differences have been determined through conventional ground surveying techniques for over a century. Since the mid-80s GPS, GLONASS and other satellite systems have modernized the means by which elevation differences are observed. In this article, we assessed the quality of GEIOD12B through long-occupation GNSS static surveys. A set of NGS benchmarks was occupied for at least one hour using dual-frequency GNSS receivers. Collected measurements were processed using a single CORS station at most 24 kilometers from the benchmarks. Geoid undulation values were driven by subtracting measured ellipsoidal heights from the orthometric heights posted on the NGS website. To assess the quality of GEOID12B, we compared our computed vertical shifts at the benchmarks with those estimated from GEOID12B published by NGS. In addition, Kriging model was used to interpolate local maps for the geoid undulations from the benchmark heights. The maps were compared with corresponding parts of GEOID12B. No biases were detected in the results and only shifts due to random errors were found. Discrepancies in the range of ten centimetres were noticed between our geoid undulation and the values available from NGS.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".