Evaluation of Landmine Clearing Mechanisms: Chain Flails and Mine Hammer
Bibliographic record
Abstract
Several mechanical demining machines employ flails as the key mechanism for neutralizing landmines. Typically, flail systems consist of a rotating drum with a series of long chains with masses attached at the end. These masses strike and mill the ground that detonates and/or fragment buried landmines. Despite flail-based technology existing for several years in the demining field, minimal studies regarding the interaction with soil have been conducted. Three chain flail systems were evaluated in the soil bin at various rotational speeds. High speed videography of single pass operations indicated that a consistent and repeatable cleared path was not obtainable. This validated the need for multiple passes to effectively clear a minefield. The results were compared with the Mine Hammer mechanism that had consistent impacts on the surface. The load distribution at the various depths was recorded and the magnitude of the impulses calculated varied with depth and level of soil compaction. The soil penetration profile was measured by recording the depth and volume of the loose soil overburden along with the geometry of the compact (hardpan) interface of the cleared path.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".