МЕТОДЫ ОЦЕНКИ БАЛАНСА УГЛЕРОДА В ЛЕСНЫХ ЭКОСИСТЕМАХ И ВОЗМОЖНОСТИ ИХ ИСПОЛЬЗОВАНИЯ ДЛЯ РАСЧЕТОВ ГОДИЧНОГО ДЕПОНИРОВАНИЯ УГЛЕРОДА
Bibliographic record
Abstract
Рассмотрены основанные на моделировании различные методы оценки и прогноза запаса углерода в лесах, которые получили международное признание, в их числе: ИЗИС IIASA (Австрия), EFIMOD + ROMUL (Россия), РОБУЛ (Россия), Информационная система определения и картирования депонирования лесами углерода (Россия, УГЛТУ), CBM-CFS3 (Лесная служба Канады), FORCARB2 (Лесная служба США). Рассчитана и проиллюстрирована в картографической форме величина годичного депонирования углерода основными типами лесных экосистем зоны хвойно-широколиственных лесов Европейско-Уральской части России. Методика расчета базируется на рекомендациях МГЭИК ООН. В расчетах использованы данные Государственного лесного реестра (ГЛР). По данным ГЛР 2015 г. выполнены расчеты годичного накопления углерода в лесных экосистемах зоны хвойно-широколиственных (смешанных) лесов ЕУЧР. Получены следующие значения показателей, характеризующих скорость накопления органического вещества: NPP = 5,92; Rh = 3,96; NEP = 1,96 т С/га · год, а NBP = 2,02 т С/га · год. Полученные нами значения годичной продукции и эмиссии углерода согласуются с результатами расчетов для этой территории, сделанными научным коллективом IIASA. Расхождение оценок находится в пределах стандартных ошибок расчетов.Various methods of assessment and forecast of the carbon sequestration by forests which are based on simulation and are recognized by scientific international community have been discussed in this article. They include the following: IIASA (Austria), EFIMOD + ROMUL (Russia), ROBUL (Russia), Information System of Definition and Carbon Mapping (Russia, Ural SFTU), CBM-CFS3 (Canadian Forest service), FORCARB2 (US Forest Service). The carbon stored in forest ecosystems of the European-Ural Part of Russia is quantified. The cartographic presentation shows the annual carbon sequestration assessments according to the main types of forest ecosystems such as coniferous, broad-leaved and deciduous forests. The valuation method is based on the annual net increment in volume with equations and methodology recommended by the IPCC UN. The State forest register data have been used to estimate the carbon balance in forest ecosystems. The evaluation of carbon sequestration for an ecosystem of coniferous-deciduous (mixed) forests in European-Ural Part of Russia was made by us in 2015, which resulted in the following data : NPP = 5,92; Rh = 3,96; NEP = 1,96 t C / ha • year and NBP = 2,02 t C / ha • year. Our values of annual carbon production and carbon emission are consistent with the results of calculations for the above area made by the IIASA’s scientific team. The estimate discrepancies are within the standard calculation errors.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.057 | 0.017 |
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".