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Record W2520616134 · doi:10.5539/enrr.v6n3p106

Nutrient Dynamics and Decomposition of Riparian Arundinaria gigantea (Walt.) Muhl. Leaves in Southern Illinois

2016· article· en· W2520616134 on OpenAlexvenueno aff
Amanda M. Nelson, Jon E. Schoonover, Karl W. J. Williard

Bibliographic record

VenueEnvironment and Natural Resources Research · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil and Water Nutrient Dynamics
Canadian institutionsnot available
FundersU.S. Fish and Wildlife ServiceU.S. Department of AgricultureNational Science Foundation
KeywordsNutrientPlant litterNutrient cycleRiparian zoneBiomass (ecology)NitrogenBotanyEnvironmental scienceAgronomyChemistryBiologyEcology

Abstract

fetched live from OpenAlex

<p class="1Body">Leaf litter quality and quantity can influence soil nutrient dynamics and stream productivity through decomposition and serving as allochthonous stream inputs. Leaf deposition, nitrogen (N)-resorption efficiency and proficiency, and decomposition rates were analyzed in riparian stands of <em>Arundinaria gigantea </em>(Walt.) Muhl.<em> </em>in southern Illinois for the first time to determine potential nutrient cycling from riparian canebrake restoration. Leaf litter was collected from five established canebrakes monthly over one year and a decomposition study was conducted over 72 weeks. Live leaves, freshly senesced leaves, and decomposed leaves were analyzed for carbon (C) and N content. Leaf litterfall biomass peaked in November at twice the monthly average for all but one site, indicating a resemblance to deciduous leaf fall patterns. Nitrogen and C concentrations decreased 48% and 30%, respectively, between live leaves and 72 weeks following decomposition. High soil moisture appeared to slow decomposition rates, perhaps due to the creation of anaerobic conditions. Cane leaves have low resorption proficiency and nutrient-use proficiency, suggesting that these riparian canebrakes are not N limited. Giant cane should be considered in multispecies riparian buffer creation since it has this potential to supply carbon and nitrogen to the soil and to macroinvertebrates in the streams for a longer period of time and year round.</p>

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 score0.034
Threshold uncertainty score0.437

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 studies0.0000.001
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 head0.012
GPT teacher head0.261
Teacher spread0.249 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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