Evaluation of Sustainable Forest and Air Quality Management and the Current Situation in Europe through Operation Research Methods
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
Forests cover 30 percent of the Earth’s land surface, almost four billion hectares, and they are necessary to sustain human health, economic growth, and environmental health. Approximately 25 percent of the global population depends on forests for food and work. The world population is expected to reach 9.6 billion by 2050. Therefore, there is a need for urgent action plans at all levels to ensure sustainable forest management and policy collaboration among all stakeholders, in order for forests to continue to serve our ecosystem and life in the future. The study compares 30 countries using 15 indicators related to forest and air quality. This was performed with TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje, meaning Multi-Criteria Optimization and Compromise Solution), which are among the most used multi-criteria decision-making methods in the literature. According to the analysis results, Denmark, Luxembourg, Lithuania, and Germany are the best performing countries in terms of indicators, whereas Slovakia, Estonia, Turkey, Latvia, Chile, and Canada are the worst performing. The paper aims to present the current situation of some developed and developing countries and compare them to each other in terms of forest and air quality indicators. In addition, the article aims to inform all stakeholders and raise awareness to achieve the Sustainable Development Goals (SDGs) and Global Forest Goals of the United Nations Strategic Plan for Forests 2017–2030 targets.
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 | 0.012 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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