The effects of modern war and military activities on biodiversity and the environment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
War is an ever-present force that has the potential to alter the biosphere. Here we review the potential consequences of modern war and military activities on ecosystem structure and function. We focus on the effects of direct conflict, nuclear weapons, military training, and military produced contaminants. Overall, the aforementioned activities were found to have overwhelmingly negative effects on ecosystem structure and function. Dramatic habitat alteration, environmental pollution, and disturbance contributed to population declines and biodiversity losses arising from both acute and chronic effects in both terrestrial and aquatic systems. In some instances, even in the face of massive alterations to ecosystem structure, recovery was possible. Interestingly, military activity was beneficial under specific conditions, such as when an exclusion zone was generated that generally resulted in population increases and (or) population recovery; an observation noted in both terrestrial and aquatic systems. Additionally, military technological advances (e.g., GPS technology, drone technology, biotelemetry) have provided conservation scientists with novel tools for research. Because of the challenges associated with conducting research in areas with military activities (e.g., restricted access, hazardous conditions), information pertaining to military impacts on the environment are relatively scarce and are often studied years after military activities have ceased and with no knowledge of baseline conditions. Additional research would help to elucidate the environmental consequences (positive and negative) and thus reveal opportunities for mitigating negative effects while informing the development of optimal strategies for rehabilitation and recovery.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it