Advances and Environmental Impact Assessment of Forest Fire Extinguishing Agents
Bibliographic record
Abstract
In the context of climate change, increasingly severe forest fires present a significant threat to lives, property, ecosystem functionality, and the sustainable development of forest resources. As a result, there is an urgent need for rapid, efficient, and environmentally friendly technologies for fire suppression and containment. This paper begins by reviewing the current research on forest fire extinguishing agents and materials that hold promise for effective fire suppression. Among these agents, gaseous and foam extinguishing materials exhibit drawbacks such as low efficiency or significant environmental hazards. In contrast, natural polymer hydrogels, which are high in water content, environmentally friendly, and biodegradable, show significant potential for developing clean and efficient extinguishing materials. Furthermore, this paper discusses existing environmental assessment standards for fire extinguishing agents, as well as the assessment systems proposed in various studies. It finds that, while universal assessment standards are fairly well-established, current research primarily focuses on enhancing fire suppression performance. However, the environmental performance assessment of forest fire extinguishing agents—often used in large quantities—remains inadequate. Therefore, there is an urgent need to establish a comprehensive and systematic environmental assessment system to address this theoretical and practical gap.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".