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Record W2196528452 · doi:10.1002/eco.1699

Hydrological controls on productivity of regenerating <i>Sphagnum</i> in a cutover peatland

2015· article· en· W2196528452 on OpenAlexafffund
Neil Taylor, Jonathan S. Price, Maria Strack

Bibliographic record

VenueEcohydrology · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsUniversity of Waterloo
FundersCanadian Sphagnum Peat Moss AssociationUniversity of Waterloo
KeywordsSphagnumPeatEnvironmental scienceProductivityBiomass (ecology)Water tableWater contentHydrology (agriculture)EcohydrologyPrecipitationSoil scienceAgronomyEcologyEcosystemGroundwaterGeologyBiologyGeography

Abstract

fetched live from OpenAlex

Abstract Recent research into the production of Sphagnum biomass in extracted peatlands, an alternative to ecological restoration as a post‐industrial use of peatlands, has highlighted the need for an improved understanding of the ecohydrology of Sphagnum regenerating in these environments. Previous work suggested that limited connectivity between surficial layers and the underlying partially decomposed plant matter and peat would result in water stress and inhibited growth. This study links the soil water dynamics of regenerated layers ranging in age from 3 to 43 years and from 3 to 40 cm in thickness to the productivity of Sphagnum in order to determine the hydrological controls on productivity and the optimal range of water content for producing Sphagnum biomass. Productivity was never observed to be limited by insufficient supply of water, including during periods where water table was &gt;40 cm below the surface and periods of 16 days without measured precipitation. While layers of different ages and thicknesses were able to sustain adequate water supply to remain productive under a range of conditions, the ability of layers to transmit water upwards differed greatly. Water content in the near‐surface inhibited productivity during wetter periods, especially at newly regenerating sites where the layer was &lt;5 cm thick. This has important implications for biomass production using the Sphagnum species studied here. Using a mixed linear modelling approach to isolate the effects of water content on variance in productivity from those of other measured variables, a volumetric water content of 0.10 is identified as optimal. Copyright © 2015 John Wiley &amp; Sons, Ltd.

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)0.001
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 score0.140
Threshold uncertainty score0.460

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.018
GPT teacher head0.235
Teacher spread0.216 · 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

Citations12
Published2015
Admission routes2
Has abstractyes

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