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Record W2769663976 · doi:10.1002/ece3.3522

Potential contributions of root decomposition to the nitrogen cycle in arctic forest and tundra

2017· article· en· W2769663976 on OpenAlexafffund
Sabrina Träger, Ann Milbau, Scott D. Wilson

Bibliographic record

VenueEcology and Evolution · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsUniversity of Regina
FundersNatural Sciences and Engineering Research Council of CanadaUmeå UniversitetUniversity of Regina
KeywordsTundraVegetation (pathology)Arctic vegetationArcticEnvironmental scienceNutrientNutrient cycleEcologyNitrogen cycleClimate changeAgronomyNitrogenBotanyBiologyChemistry

Abstract

fetched live from OpenAlex

Plant contributions to the nitrogen (N) cycle from decomposition are likely to be altered by vegetation shifts associated with climate change. Roots account for the majority of soil organic matter input from vegetation, but little is known about differences between vegetation types in their root contributions to nutrient cycling. Here, we examine the potential contribution of fine roots to the N cycle in forest and tundra to gain insight into belowground consequences of the widely observed increase in woody vegetation that accompanies climate change in the Arctic. We combined measurements of root production from minirhizotron images with tissue analysis of roots from differing root diameter and color classes to obtain potential N input following decomposition. In addition, we tested for changes in N concentration of roots during early stages of decomposition, and investigated whether vegetation type (forest or tundra) affected changes in tissue N concentration during decomposition. For completeness, we also present respective measurements of leaves. The potential N input from roots was twofold greater in forest than in tundra, mainly due to greater root production in forest. Potential N input varied with root diameter and color, but this variation tended to be similar in forest and tundra. As for roots, the potential N input from leaves was significantly greater in forest than in tundra. Vegetation type had no effect on changes in root or leaf N concentration after 1 year of decomposition. Our results suggest that shifts in vegetation that accompany climate change in the Arctic will likely increase plant-associated potential N input both belowground and aboveground. In contrast, shifts in vegetation might not alter changes in tissue N concentration during early stages of decomposition. Overall, differences between forest and tundra in potential contribution of decomposing roots to the N cycle reinforce differences between habitats that occur for leaves.

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 score0.072
Threshold uncertainty score0.144

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
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 head0.011
GPT teacher head0.249
Teacher spread0.239 · 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

Citations8
Published2017
Admission routes2
Has abstractyes

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