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Record W2724896786 · doi:10.1101/158733

Spring frost controls spring tree phenology along elevational gradients on the southeastern Tibetan Plateau

2017· preprint· en· W2724896786 on OpenAlexaff
Yafeng Wang, Bradley S. Case, Sergio Rossi, Liping Zhu, Eryuan Liang, Aaron M. Ellison

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2017
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicTree-ring climate responses
Canadian institutionsUniversité du Québec à Chicoutimi
FundersChinese Academy of SciencesNational Natural Science Foundation of China
KeywordsPhenologyFrost (temperature)Spring (device)Plateau (mathematics)Climate changeEnvironmental scienceGlobal warmingClimatologyAtmospheric sciencesPhysical geographyEcologyGeographyBiologyGeologyMeteorology

Abstract

fetched live from OpenAlex

Abstract Temperature is considered to be a main driver of spring phenology, whereas the role of climate extremes (such as spring frosts) has long been neglected. A large elevational gradient of mature forests on the Tibetan Plateau provides a powerful space-for-time ‘natural experiment’ to explore driving forces of spring phenology. Combining 5-yr of in situ phenological observations of Smith fir ( Abies georgei var. smithii ) with concurrent air temperature data along two altitudinal gradients on the southeastern Tibetan Plateau, we tested the hypothesis that spring frost was a major factor regulating the timing of spring phenology. Onset of bud swelling and leaf unfolding in the study years occurred ≈ 18 or 17 days earlier, respectively, at the lowest (3800 m a.s.l.) elevation relative to upper treelines (4360 or 4380 m a.s.l.). The frequency of freezing events and last freezing date were critical factors in determining the timing of bud swelling along two altitudinal gradients, whereas onset of leaf unfolding was primarily controlled by the onset of bud swelling. This finding provides evidence for detrimental impacts of spring frost on spring phenology, which have been underappreciated in research on phenological sensitivity to climate but should be included in phenology models. It contributes to explain the declining global warming effects on spring phenophases, because climatic extreme events (e.g. spring frosts) tend to increase with warming.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient 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 score0.010
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0020.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.025
GPT teacher head0.222
Teacher spread0.196 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

Explore more

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