Analysis of Foreign Experience in National Forest Inventories: Methods, Sampling, Results and International Statistics
Bibliographic record
Abstract
Выполнен анализ многолетнего опыта ведения национальных инвентаризаций лесов (НИЛ) за рубежом. Рассмотрены принципы построения сети НИЛ, особенности организации выборочных полевых измерений, методы интерпретации первичной информации, получение итоговых оценок и использование результатов для стратегического планирования производства древесного сырья, разработки национальных программ развития лесного хозяйства и организации управления лесами. Зарубежный опыт детально рассмотрен на примере стран с преимущественно бореальными и лесами умеренной зоны: США, Канада, страны ЕС - Скандинавские страны (Норвегия, Швеция, Финляндия) и Чехия. Новые тенденции развития НИЛ связаны с изменением стратегической направленности, дополнением измеряемых и оцениваемых показателей, интеграцией НИЛ и систем мониторинга, гармонизацией терминологии и методов ведения, совершенствованием технологии, адаптацией к требованиям обязательной и добровольной отчетности по лесам в международных процессах. The article conducts analysis of foreign experience in national forest inventories (NFI), in particular: principles, sampling procedures, data collecting on sample plots, methods of interpreting primary information and reporting, using the results for strategic planning of wood production and national forest policy and also for sustainable forest management. Foreign experience is considered in detail on example of countries with predominantly boreal and temperate forests: the USA, Canada, EU-Scandinavian countries (Norway, Sweden and Finland) and the Czech Republic. The new trends in the elaboration of national forest inventories are associated with a change in strategy, complementing the measured and assessed indicators, integration with monitoring system, harmonization of the terminology and methods of maintenance, improvement of the technology, and adaptation to the requirements of mandatory and voluntary forestry reporting in international processes.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".