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Record W3159883661 · doi:10.1139/as-2020-0016

Interspecific and intraspecific variation in leaf toughness of Arctic plants in relation to habitat and nutrient supply

2021· article· en· W3159883661 on OpenAlexvenueno aff
Ned Fetcher, Sofia Iglesia, Stephen J. Turner, Thomas C. Parker

Bibliographic record

VenueArctic Science · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsnot available
FundersOffice of International Science and EngineeringCentro Nacional de Investigaciones CardiovascularesNational Science Foundation
KeywordsTundraTussockBiologyInterspecific competitionBotanyEcologyAgronomyArctic

Abstract

fetched live from OpenAlex

Leaf toughness is an important functional trait that confers resistance to herbivory and mechanical damage. We sought to determine how species composition, climate, seasonality, and nutrient availability influence leaf toughness in two types of tundra in northern Alaska. We measured leaf toughness as force to punch for 11 species of Arctic plants in tussock tundra and dry heath tundra at 17 sites distributed along a latitudinal gradient. Rubus chamaemorus L. and the graminoids occupied opposite ends of the leaf toughness spectrum, with Rubus chamaemorus requiring the least force to punch, whereas one of the graminoids, Eriophorum vaginatum L., required the most. Leaf toughness increased with mean summer temperature for Eriophorum vaginatum and Betula nana L., whereas it declined with warmer temperatures for the other species. Toughness of mature leaves of Eriophorum vaginatum did not vary through the growing season but declined significantly after senescence. Application of N and P fertilizer in an experimental site decreased leaf toughness in three species but had no effect on four others. Leaf toughness of four out of five species in dry heath was greater than for the same species in tussock tundra, but there was no difference in community-weighted mean toughness between tussock tundra and dry heath.

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 score0.023
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.028
GPT teacher head0.240
Teacher spread0.212 · 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

Citations3
Published2021
Admission routes1
Has abstractyes

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