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Record W3197479348 · doi:10.21203/rs.3.rs-849966/v1

An Environment Transitional Zone Buffers Peatlands Carbon Loss

2021· preprint· en· W3197479348 on OpenAlexaff
Liangfeng Liu, Huai Chen, Jianqing Tian, Hongjun Wang, Dan Xue, Ning Wu, Meng Wang, Xingliang Xu, Changhui Peng, Yanfen Wang

Bibliographic record

VenueResearch Square · 2021
Typepreprint
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsPeatCarbon fibersEnvironmental scienceSoil scienceEcologyMaterials scienceBiology

Abstract

fetched live from OpenAlex

Abstract An environment transitional zone (ETzone) is usually deemed as a hotspot in biogeochemical cycle, but little is known about its response to climate change. A typical ETzone develops at the subsurface of peatland after experiencing long-term water table fluctuation, characterized by alternative aerobic and anaerobic conditions. By an extensive incubation, we found that the CO2 emission at this ETzone was 28.31 ± 3.55 μg g-1 d-1, 41.6% and 34.4% lower than the upper (aerobic) and lower (anaerobic) layers, respectively. Moreover, with a lowest Q10 of 1.37, its CO2 emission was also the least warming-responsive, which could reduce 33.7% CO2 loss in warming scenario. This result clearly revealed that the ETzone worked as a buffer to retard carbon loss, rather than a hotspot. Surprisingly, this buffer capacity of ETzone was easily collapse if being primed by fresh carbon. Therefore, maintaining a relatively stable ETzone is critical for protecting peatland carbon stock, and the priority is to block priming effect through maintaining an intact vegetation composition.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.027
GPT teacher head0.319
Teacher spread0.291 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2021
Admission routes1
Has abstractyes

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