Comparison of thermal and radar active layer measurement techniques in the Leaf Bay area, Nouveau-Québec
Bibliographic record
Abstract
Continuous profiles of the frost table for a series of traverses in unconsolidated sediments in the Leaf Bay area, Ungava, were obtained using an all terrain vehicle borne impulse radar system. The results correlate extremely well with data obtained by the insertion of rigid temperature probes at frequent intervals along the traverses. Each of the two systems has its own particular advantages and disadvantages. The radar profiling system provides more continuous information and can be carried out more rapidly than the system using the temperature probes. However, it requires some local ground control as regards soil moisture profiles and values for soil texture and soil temperature. A definite advantage of the rigid temperature probe is that it produces a temperature curve as a by-product and this can be used to assess the effect of different terrain factors on heat flow in the soil. It is, furthermore, a much cheaper and less bulky apparatus than the radar equipment, requiring no auxiliary logistical support for field use. Neither method can be applied universally for thaw zone and active layer determination. The trials in the tidal flats were a failure, the earth materials being too stony for the temperature probe and the salt water in the soil not permitting proper signal propagation for the radar. Neither method is suitable for penetrating bedrock and, in such a case, reliance has to be placed on temperature measurements in drillholes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".