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Assessing the Possible Risks of Including the Reserved Forests in National Reporting under the UN Convention on Climate Change

2021· article· ru· W4400953477 on OpenAlexaboutno aff
А.Н. Филипчук, Н.В. Малышева, Т.А. Золина, А.Н. Югов, Р.Ю. Миронов

Bibliographic record

VenueLesohozâjstvennaâ informaciâ · 2021
Typearticle
Languageru
FieldEnvironmental Science
TopicEnvironmental Impact and Sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsClimate changeConventionEnvironmental resource managementEnvironmental scienceGeographyPolitical scienceEcologyLawBiology

Abstract

fetched live from OpenAlex

Дана оценка рисков включения резервных лесов в состав управляемых лесных земель для составления Национального доклада Российской Федерации о кадастре антропогенных выбросов из источников и абсорбции поглотителями парниковых газов, не регулируемых Монреальским протоколом. Рассмотрены принципы и критерии выделения управляемых лесов, которые применяются при подготовке национальных кадастров парниковых газов (ПГ) в разных странах мира и России. Приведена многолетняя динамика запасов, абсорбции, потерь и баланса углерода в биомассе резервных лесов страны. Выполнена сравнительная оценка ежегодных потерь углерода в биомассе резервных лесов, вызванных гибелью от пожаров, с потерями в защитных и эксплуатационных лесах. Подтверждено расчетами, что при современном уровне пожарных эмиссий углерода и отсутствии потерь от заготовки древесины включение резервных лесов в Национальный кадастр ПГ увеличит оценку запаса углерода в биомассе лесов страны на 17%, годичную абсорбцию – на 13%, а общую национальную оценку нетто-поглощения углерода биомассой лесов – на 13%. An assessment of the risks of including the reserved forests in managed forest land for the Russian Federation National Inventory Report of anthropogenic emissions by sources and removals by sinks of greenhouse gases not controlled by the Montreal Protocol is given. The principles and criteria for identifying the managed forests that applied in the preparation of national greenhouse gas (GHG) inventories around the world and in Russia are reviewed. Long-term trends in carbon stocks, carbon absorption, carbon losses and carbon balance of reserved forest are presented. A comparative assessment of the annual carbon losses in the biomass of reserved forests caused by fire mortality with those in protective and exploitable forests named as managed forests has been made. Confirmed by calculations that, given the current level of fire emissions and the absence of losses from timber harvesting, inclusion of reserved forests in the National GHG Inventory will increase the estimate of carbon stock in forest biomass by 17%, annual absorption by 13%, and net carbon sequestration of forest biomass in total by 13%.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.255
GPT teacher head0.430
Teacher spread0.175 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2021
Admission routes1
Has abstractyes

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