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Record W4406638912 · doi:10.1139/cjb-2024-0064

Effects of afforestation drainage on <i>Sphagnum</i> spore banks

2025· article· en· W4406638912 on OpenAlexvenueno aff
Xu Chen, Yun-He Yang, Jiaqi Zhang, Jianyi Wang, Zhao‐Jun Bu

Bibliographic record

VenueBotany · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBryophyte Studies and Records
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsSphagnumBiologyAfforestationSporeBotanyDrainagePeatEcology

Abstract

fetched live from OpenAlex

Persistent spore banks may play a significant role in the maintenance of bryophyte populations. However, the impact of afforestation drainage on Sphagnum spore banks remains rarely known. In two drained peatlands of northeastern China, peat cores were obtained from near-pristine (high water table— Sphagnum), moderately drained (low water table— Sphagnum), and severely drained areas (low water table—shrub) for Sphagnum spore bank analysis. Quantitative properties of spore banks were compared among the areas with different drainage intensity. Our results indicate that drainage experienced by the two peatlands generally reduced the spore accumulation rate in the peatlands, and the intensified aerobic decomposition following drainage also led to spore bank loss. After drainage, the germinability of the Sphagnum spores was significantly enhanced probably due to dormancy releasing. This may deplete Sphagnum spore bank in the long run. Our study suggests that rewetting practice in drained peatlands is needed to conserve Sphagnum spore banks and maintain the potential for restoring peatland vegetation after serious disturbance.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.011

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.004
GPT teacher head0.198
Teacher spread0.195 · 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
Published2025
Admission routes1
Has abstractyes

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