Acoustic landmine detection at 40° below zero
Bibliographic record
Abstract
To study the effects of snow cover and frozen ground, mine detection experiments based on acoustic to seismic (A/S) coupling have been conducted on a roadway at the Defense Research Establishment Suffield (DRES), Alberta, Canada, 5–16 February 2001. The site is a gravel road with grain or gravel sizes as large as several centimeters. The ground was frozen. Only a few measurements of acoustic-to-seismic coupling in frozen grounds have been reported. The coupling ratio is within a factor of 10 of that for unfrozen soils. The fast compressional wave speeds were expected to be significantly larger than those in unfrozen soils, while slow compressional wave speeds were expected to be possibly unchanged, except for temperature effects. Therefore, one might expect significantly larger wavelengths for the fast compressional wave and nonlocally reacting coupling. Compensation for these changes included using higher-frequency sound and normal incidence angles for the sound source. This presentation outlines the results of this testing. [Work supported by the U.S. Army Communcations–Electronics Command Night Vision and Electronic Sensors Directorate.]
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".