Ovire in rešitve pri sanaciji v ujmah poškodovanih zasebnih gozdov
Bibliographic record
Abstract
Frequent natural disasters in recent years have been a major challenge in private forest management and have led to increased activity among all stakeholders along the forest-wood chain. In this paper, we reviewed the literature on salvage logging in private forests damaged by natural disasters, with the aim of identifying the barriers that private forest owners face in salvaging and solutions for faster and more efficient salvaging. After reviewing the relevant literature, we included 59 articles and 25 reports in the final analysis. The results showed that researchers have not yet systematically addressed the identification of barriers. We identified 51 barriers, which we classified into 7 groups, and 68 solutions, which we classified into 11 groups. Most researchers have dealt with barriers from the 'Characteristics of private forest owners' group and solutions from the 'Stakeholder Cooperation' group. Finally, we associated the identified barriers with appropriate salvaging solutions and found that all identified solutions represent a solution for at least one of the barriers and that each barrier has at least one solution. The research represents the first, but important, step in identifying the decision-making factors for salvaging in private forests damaged by natural disasters.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".