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Record W2102849773 · doi:10.2980/16-2-3128

Examining the peat-accumulating potential of fen vegetation in the context of fen restoration of harvested peatlands

2009· article· en· W2102849773 on OpenAlexaffvenue
Martha D. Graf, Line Rochefort

Bibliographic record

VenueEcoscience · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsPeatBryophyteSphagnumBogBotanyBiomass (ecology)Vegetation (pathology)HalophyteBiologyEnvironmental scienceEcology

Abstract

fetched live from OpenAlex

In order to focus efforts towards specific vegetation groups in fen restoration, knowledge of the peat-accumulating function of dominant fen species is critical. The decomposition rates of 3 species typical to undisturbed fens and 3 species that spontaneously colonize harvested fens were assessed. These species were incubated in both a restoration site (harvested fen) and an undisturbed fen to compare decomposition according to different environmental conditions. The average exponential decay coefficient (k) for all material types was slightly higher (circa 0.04·y−1 higher) in the harvested fen than those observed in the undisturbed fen. However, the litter type (leaves, roots/rhizomes, or bryophyte fragments) had the largest impact on the decomposition rates. The 2 tested bryophytes had lower k-values (between 0.14 and 0.11 for Polytrichum strictum and 0.06 for Sphagnum centrale) than the vascular plant litter (between 0.25 and 0.50). The annual primary production of the tested species was also measured to estimate the peat-accumulating capacity of each species. Scirpus cyperinus had an annual primary production that was 3 times higher (1500 g·m−2.y−1) than the other species (between 300 and 550 g·m−2.y−1). Estimates show that the harvested fen has a high peat-accumulating potential due to the high biomass production observed at this site.

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 score0.010
Threshold uncertainty score0.019

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)0.0000.000

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 head0.025
GPT teacher head0.253
Teacher spread0.228 · 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

Citations46
Published2009
Admission routes2
Has abstractyes

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