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Record W2395364234 · doi:10.1111/gcb.13360

Bud break responds more strongly to daytime than night‐time temperature under asymmetric experimental warming

2016· article· en· W2395364234 on OpenAlexafffundabout
Sergio Rossi, Nathalie Isabel

Bibliographic record

VenueGlobal Change Biology · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicPlant Water Relations and Carbon Dynamics
Canadian institutionsCanadian Forest ServiceNatural Resources CanadaUniversité du Québec à Chicoutimi
FundersNatural Resources CanadaCanada Foundation for Innovation
KeywordsDaytimeEnvironmental scienceAtmospheric sciencesClimate changeGlobal warmingWarming upClimatologyEcologyBiologyPhysicsGeology

Abstract

fetched live from OpenAlex

Global warming is diurnally asymmetric, leading to a less cold, rather than warmer, climate. We investigated the effects of asymmetric experimental warming on plant phenology by testing the hypothesis that daytime warming is more effective in advancing bud break than night-time warming. Bud break was monitored daily in Picea mariana seedlings belonging to 20 provenances from Eastern Canada and subjected to daytime and night-time warming in growth chambers at temperatures varying between 8 and 16 °C. The higher advancements of bud break and shorter times required to complete the phenological phases occurred with daytime warming. Seedlings responded to night-time warming, but still with less advancement of bud break than under daytime warming. No advancement was observed when night-time warming was associated with a daytime cooling. The effect of the treatments was uniform across provenances. Our observations realized under controlled conditions allowed to experimentally demonstrate that bud break can advance under night-time warming, but to a lesser extent than under daytime warming. Prediction models using daily timescales could neglect the diverging influence of asymmetric warming and should be recalibrated for higher temporal resolutions.

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 score0.445
Threshold uncertainty score0.999

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)0.0010.001

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 head0.014
GPT teacher head0.255
Teacher spread0.241 · 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

Citations88
Published2016
Admission routes3
Has abstractyes

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