Plastic pollution in shooting ranges and warfare areas - an overlooked environmental issue
Bibliographic record
Abstract
Shooting ranges and military training fields, including warfare-impacted areas, have been widely recognized as environmentally impacted zones by inorganic and organic contamination, such as heavy metals, polycyclic aromatic hydrocarbons or explosive-related compounds. However, the possible contamination by plastics and microplastics in soil has been widely overlooked despite potential plastic sources, such as shotgun cartridges, plastic wads or landmines. Due to how these activities occur, plastics have remained in the field for decades or centuries, favoring their conversion from macro to microplastics, polluting the soil and water resources. Moreover, shooting and recreational activities such as airsoft or paintball practices could also be a substantial source of plastics to ecosystems; once shot, pellets can have conventional or biodegradable plastics in their composition, and there left in the environment, favouring impacts on soil properties. Although some initiatives have emerged to avoid the use of single-use plastics in shotgun ammunition, alternative materials (biodegradable plastics) can also be a potential risk, favouring the heavy metal bioavailability of shot pellets. These emerging pollutants should also be considered in these areas to understand if they could be a potential source of micro- and nanoplastics to the environment and, therefore, an environmental concern that requires changes at industrial and regulatory levels.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".