Handbook for resource and energy efficiency in forest-based industries of Eastern Europe. A practical guide on how to improve cleaner production in woodworking SMEs. RERAM report D6.3. English version
Bibliographic record
Abstract
The growing demand for raw wood material supply in the EU is increasing pressure on forest resources in the ENP target region. Non-sustainable forestry and uncontrolled timber harvesting have led to a significant loss of forest area in the Carpathians (Ukraine) and the Caucasus (Georgia, Armenia, Azerbaidjan) with far reaching impacts. The RERAM project targets the local wood processing industries, which consume substantial volumes with high losses of the raw wood material. By raising awareness on resource efficiency and valorising hands-on knowlegde and solutions as well as training and coaching to local entrepreneurs, the project seizes a major opportunity to improve clean production and climate change mitigation in these emerging hotspot forest regions. This Handbook is a practical guide for managers and technical personnel on how to improve production efficiency with the goal to save input costs and reduce environmental impacts at the same time. Most woodworking companies are not aware that inefficient production generates large losses of material and energy and sum up to considerable costs. Wastes and emissions once were input materials, which were bought for money, but have not been converted into products to be sold for money. This handbook introduces Cleaner Production principles, tools and improvement options that can leverage a variety of saving potentials in SMEs. The solutions can easily be applied in any company. The handbook has been published in English, Ukrainian, Romanian and Georgian language: EN version https://doi.org/10.5281/zenodo.4008070 | UA version https://doi.org/10.5281/zenodo.11820198 | RO version https://doi.org/10.5281/zenodo.11811165 | GE version https://doi.org/10.5281/zenodo.11824023
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How this classification was reachedexpand
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 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".