Сравнительная характеристика лесов и ведения лесного хозяйства в разных странах
Bibliographic record
Abstract
Analysis of forest conditions and forest management in 15 states (14 foreign states and the Russian Federation). Such choice for analysis is determined by principal differences in forest management and forest use in these states and forest ecosystems due to locations. 3 Forests in Brazil, Indonesia, Malaysia tropical belt; 3 Forests in Argentina, India, Uruguay tropical and subtropical belts ; 3 Forests in Australia tropical, subtropical and moderate belts; 3 New Zealand forests subtropical and moderate belts; 3 Forests in China, Russia, USA, Canada, Finland, Sweden, Germany moderate and boreal belts. FAO forest resources assessment data in 1990, 2000, 2005 and 2010 has been applied in the analysis. All national forest resource indicators have been evaluated under the FAO procedure and worked out to the uniform format. That ensures reliability of comparative assessments. National data may differ from those available in FAO data bases. Criteria and indicator set ensures similar national approaches in monitoring, assessment and presentation of data on trends in forest conditions with regard to a total range of forest ecosystem values and national progress in sustainable forest management. Criteria and indicator applications enable integrated approach in forest management. The work has been done in the run-up of 2015 Global assessment of forest resources release. The comparative characteristics is deemed to go on.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".