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Record W4225262964 · doi:10.1111/rec.13716

Vegetative growth and belowground expansion from transplanted low‐arctic tundra turfs

2022· article· en· W4225262964 on OpenAlexafffundabout
Ian G. Hnatowich, Eric G. Lamb, Katherine Stewart

Bibliographic record

VenueRestoration Ecology · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTundraBiomass (ecology)TransplantationEcosystemRestoration ecologyBiologyPlant communityEcologyArcticEnvironmental scienceEcological succession

Abstract

fetched live from OpenAlex

Whole‐turf transplantation is a restoration method used to restore plant communities within disturbed arctic environments. Transplant expansion and restoration success is often determined based on aboveground characteristics, and to our knowledge, this is the first investigation of belowground expansion from transplanted turfs. In this growth chamber experiment, turfs harvested from undisturbed tundra near Rankin Inlet, Nunavut, Canada, were exposed to fertilized and unfertilized substrates to determine the effect of adjacent nutrient‐enrichment on plant community composition within the turfs and substrates, as well as above and belowground biomass and expansion. Next‐generation sequencing was used to determine the species identity of expanding roots. Our results show that fertilization of substrates surrounding tundra transplants did not alter the community composition of the turfs, but did increase biomass and expansion, as well as biological soil crust cover on the adjacent substrate. Belowground biomass far exceeded aboveground, revealing the importance of evaluating belowground roots and rhizomes that dominate the vegetative biomass within arctic ecosystems. Investigation of belowground development is likely to provide holistic interpretations of restoration success and should not be ignored in future transplantation studies.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.994

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)0.0070.000

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 head0.021
GPT teacher head0.223
Teacher spread0.202 · 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.

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

Citations9
Published2022
Admission routes3
Has abstractyes

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