Bibliographic record
Abstract
The effective and sustainable environmental performance of a business is a result of various factors and most important the integrated outcome of the environmental management. Likewise, the integration of environmental protection into the armed forces functions has also gain interest for the military sector internationally. Therefore, the environmental management system (EMS) is recognized as one of the most widely used tools. This study provides a review on environmental management issues related to the military activities and their assessment globally. The multitasking characteristics of the defence sector result in the need for the eco-friendly related issues to be directed in a holistic and integrated way, with the help of a certified environmental management system. North Atlantic Treaty Organization (NATO) itself and NATO countries, like USA, UK, Canada, Holland, Denmark, Czech Republic, Greece, as well as non-NATO countries like Sweden and Australia have an environmental management system structure in place to assist military environmental management and studies reveal that the armed forces could anticipate positive outcomes from environmental management system. A case-by-case approach, of the above, is examined and based on the results, appropriate recommendations are presented, which may contribute to the environmental management system considerations as the most important tool for effective management framework and most importantly to evaluate its effectiveness as a structure for the defence sector’s activities.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".