Munitions and explosives of concern: international governance and applications for the United States
Bibliographic record
Abstract
A combination of 20th century warfare alongside the storage of and frequent testing of munitions by various national armed forces has contributed to a legacy of unexploded ordnance, munitions, and explosives of concern (MEC). The presence of such latent munitions has potentially debilitating or even fatal effects upon a generally unsuspecting stakeholders where communities may be unaware of the risks posed by buried shells, bombs, and other ordnance on both public and privately held properties. As such, various governments have undertaken differing initiatives to assess, mitigate, and manage the risks associated with these munitions. MEC remediation is generally tailored to each nation's unique historical experience with munitions and ordnance and is highly dependent not only on the type and quantity of MEC but also on the existing or proposed land use of the parcel as well. This paper compares the MEC management efforts of the United States, the United Kingdom, Germany, and Canada with regard to their MEC monitoring, detection, and removal methods in order to identify successful policies and procedures that can inform international MEC management.
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 | 0.003 | 0.005 |
| 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 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".