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Record W2806548462 · doi:10.3390/f9060315

Non-Structural Carbohydrate Dynamics in Leaves and Branches of Pinus massoniana (Lamb.) Following 3-Year Rainfall Exclusion

2018· article· en· W2806548462 on OpenAlexfundno aff
Tian Lin, Huaizhou Zheng, Zhihong Huang, Jian Wang, Jinmao Zhu

Bibliographic record

VenueForests · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicPlant Water Relations and Carbon Dynamics
Canadian institutionsnot available
FundersNatural Science Foundation of Fujian ProvinceUniversity of British ColumbiaNational Natural Science Foundation of China
KeywordsPinus massonianaDrought stressPhenologyBiologyStarchCarbohydrateHorticultureBotanyAgronomyFood scienceBiochemistry

Abstract

fetched live from OpenAlex

Drought-induced tree mortality is an increasing and global ecological problem. Stored non-structural carbohydrates (NSCs) may be a key determinant of drought resistance, but most existing studies are temporally limited. In this study, a 3-year 100% rainfall exclusion manipulation experiment was conducted to evaluate the response of NSC dynamics to drought stress in 25-year-old Pinus massoniana leaves and branches. The results showed: (1) compared with the control condition, leaf NSC concentration in the drought treatment increased 90% in the early stage (days 115–542) (p < 0.05), and then decreased 15% in the late stage (days 542–1032), which was attributed to water limitation instead of phenology; (2) the response of leaf NSCs to drought was more significant than branch NSCs, demonstrating a time lag effect; and (3) the response of P. massoniana to mild drought stress was to increase the soluble sugars and starch in the early stage, followed by an increase in soluble sugars caused by decreasing starch in the later stress period. Considering these results, mid-term drought stress had no significant effect on the total NSC concentration in P. massoniana, removing carbon storage as a potential adaptation to drought stress.

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 score0.224
Threshold uncertainty score0.990

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 head0.004
GPT teacher head0.205
Teacher spread0.201 · 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

Citations22
Published2018
Admission routes1
Has abstractyes

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