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Record W2151498404 · doi:10.1139/cjb-2013-0319

Increases in aboveground biomass and leaf area 85 years after drainage in a bog

2014· article· en· W2151498404 on OpenAlexafffundvenue
Julie Talbot, Nigel T. Roulet, Oliver Sonnentag, Tim R. Moore

Bibliographic record

VenueBotany · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsMcGill UniversityUniversité du Québec à Montréal
FundersCanadian Foundation for Climate and Atmospheric SciencesFonds Québécois de la Recherche sur la Nature et les TechnologiesBIOCAP Canada
KeywordsSphagnumBogBiomass (ecology)PeatShrubEnvironmental scienceLeaf area indexAgronomyMossDeciduousBiologyCarbon sinkBotanyEcologyEcosystem

Abstract

fetched live from OpenAlex

Climate change scenarios suggest that northern peatlands could become drier. To address the type and magnitude of vegetation change associated with persistent drying, we studied changes in biomass and leaf area index following drainage 85 years previously of a bog, using destructive sampling, allometric relationships, and optical measurements. Our results show a 10-fold increase in aboveground biomass between the reference site and the most severely drained site, resulting from the growth of a tree layer. The total leaf biomass increased slightly as a result of drainage, thus an increase in woody biomass was the main cause of the increase in aboveground biomass. Leaf area index approximately tripled in sites where trees grew. Sphagnum L. moss biomass decreased from 120 g·m −2 at the reference site (20% of all aboveground biomass) to 8 g·m −2 under the tree canopy (<1% of all aboveground biomass). The percentage of deciduous shrubs increased from 3% of the total shrub biomass in the reference site to 72% in the most severely drained site. Our results show that lowering the water table of a bog can have a profound effect on vegetation but the net effect of these changes on the role of the peatland as a carbon sink remains difficult to assess.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.013
Threshold uncertainty score0.837

Codex and Gemma teacher scores by category

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 head0.006
GPT teacher head0.200
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 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

Citations18
Published2014
Admission routes3
Has abstractyes

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