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Record W3214228005 · doi:10.1139/as-2020-0043

Shading decreases and delays NDVI and flowering of prostrate Arctic shrubs

2021· article· en· W3214228005 on OpenAlexvenueno aff
Jeremy L. May, Steven F. Oberbauer, Steven Unger, Matthew Simon, Katlyn R. Betway, Robert D. Hollister

Bibliographic record

VenueArctic Science · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsnot available
FundersNational Science Foundation
KeywordsShadingTundraShrubDeciduousPhenologyCanopyGrowing seasonEnvironmental scienceNormalized Difference Vegetation IndexVegetation (pathology)ArcticBiologyAgronomyHorticultureBotanyLeaf area indexEcology

Abstract

fetched live from OpenAlex

Increases in shrub growth and canopy cover are well documented community responses to climate warming in the Arctic. An important consequence of larger deciduous shrubs is shading of prostrate plant species, many of which are important sources of nectar and berries. Here, we present the impact of a shading experiment on two prostrate shrubs, Vaccinium vitis-idaea L. and Arctous alpina L., in northern Alaska over two growing seasons. We implemented three levels of shading (no shade, 40% shade, and 80% shade) in dry heath and moist acidic tundra. Plots were monitored for soil moisture content, surface temperature, normalized difference vegetation index (NDVI), and flowering. Shading was shown to, on average, lower surface temperature (0.7 °C to 5.3 °C) and increase soil moisture content (0.5% to 5.6%) in both communities. Both species- and plot-level NDVI values were delayed in timing of peak values (7 to 13 days) and decreased at the highest shading. Flower abundance of both species was lower in shaded plots and peak flowering was delayed (3 to 8 days) compared with controls. Changes in timing may result in phenological mismatches and can impact other trophic levels in the Arctic as both the flowers and resulting berries are important food sources for animals.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.847

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)0.0010.000

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 head0.030
GPT teacher head0.243
Teacher spread0.213 · 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

Citations11
Published2021
Admission routes1
Has abstractyes

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