Measuring gravity on ice: An example from Wanapitei Lake, Ontario, Canada
Bibliographic record
Abstract
Abstract Large lakes have always represented a problem for regional gravity databases; the difficulty of access means gaps or coarse spacing in the sampling. Satellite, airborne, and shipborne gravity techniques are options, but the resolution and/or cost of these systems make them impractical or inaccurate for exploration or environmental studies, where the required resolution is <0.1mGal/km. In this study, the feasibility of a ground gravity survey over a frozen lake where ice moves because of windy conditions is assessed. Lake Wanapitei, widely accepted as resulting from the impact of a meteorite 37 million years ago, is one of these cases in which the necessity of expanding coverage over poorly sampled regions arose from a significant gap between surface and airborne geophysical maps. Two gravity surveys were completed on the ice of Lake Wanapitei in the winters of 2003 and 2004. To study the structure, longtime series of gravity field measurements were recorded for 98 stations, allowing for improved control over the noise sources in the data. Final processing and integration with an existing regional data set in the area and the application of terrain corrections reduced the amplitude of the circular anomaly from 15 to 9mGal and its diameter from 11 to 6km. The feasibility of gravity surveys on ice was assessed, and we determined that for large-scale studies such as this one, the quality of the data, even under noisy conditions, was acceptable. However, for more detailed mapping, calm wind conditions and long time series are required.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".