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Record W2985405087 · doi:10.1093/jxb/erz505

Spring temperatures affect senescence and N uptake in autumn and N storage for winter in Rhynchospora alba (Cyperaceae)

2019· article· en· W2985405087 on OpenAlexafffund
Kyelle Byne, Peter Ryser

Bibliographic record

VenueJournal of Experimental Botany · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicPlant Water Relations and Carbon Dynamics
Canadian institutionsLaurentian University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsOverwinteringCyperaceaeSenescenceBiologyNutrientBotanyHorticulturePoaceaeEcology

Abstract

fetched live from OpenAlex

Environmental and physiological factors underlying variation in timing of autumn senescence are not well known. We investigated how the time of the onset of the growth in spring affects senescence and its functional consequences for nitrogen (N) uptake in autumn and storage of N for the winter, in a species that each year develops its bulbils for storage and overwintering anew. Rhynchospora alba was grown outdoors with two treatments, identical except for a 3 week difference in the start of growth in May. Leaf and root growth and senescence, and N uptake were recorded from August to November. By August, late-starting plants had caught up in size and total N content, but had smaller bulbils. They had a higher δ 13C, indicating a higher stomatal conductance during growth. Leaf and root senescence were delayed, extending 15N tracer uptake by 4 weeks. Nevertheless, after senescence, plants with an early start had 55% more N in their overwintering bulbils, due to earlier and more efficient remobilization. We conclude that timing of senescence in R. alba is a result of an interplay between the status of winter storage and cold temperatures, constrained by a trade-off between prolonged nutrient uptake and efficient remobilization of nutrients.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.548
Threshold uncertainty score0.356

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)0.0000.000

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 head0.005
GPT teacher head0.228
Teacher spread0.222 · 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
Published2019
Admission routes2
Has abstractyes

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