Massive Ice and Ice-Rich Soil Detection by Gravimetric Surveying at Dry Creek, Southwestern Yukon Territory, Canada
Bibliographic record
Abstract
Gravity measurements were made using a Scintrex CG-5 Autograv gravimeter in permafrost terrain conditions where bodies of buried ice were expected. The main objective was to assess the feasibility of using gravimetric technology to detect massive ice bodies at Dry Creek, Yukon Territory, Canada. Tests at 11 gravimetric survey lines and 10 boreholes were performed during the summers of 2013 and 2014. Residual anomaly profiles provide a quick first estimate of the amount of ice in underlying soil strata. A mean anomaly of −0.10 mGal was found near a thermokarst, south of the Dry Creek rest area along the Alaska Highway. The cryostratigraphy comprises at depth an ice-rich diamicton covered by massive ice with suspended silty inclusions and an overlying layer of glacio-fluvial gravel. At the base of a small glacio-fluvial hill deposit, on the southeast side of the road, a mean anomaly of −0.260 mGal was found. A massive ice body of 9.3 m of maximum thickness was drilled through. Since drilling is planned after gravimetric surveys, it is possible to assess whether or not this technique is effective for detecting sub-surface ice features. This paper presents an overview of the site, the gravimetric detection technique employed, and gravimetric and borehole test results and analysis.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".