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Record W2169618405 · doi:10.1139/x07-164

Different adaptive responses of leaf physiological and biochemical aspects to drought in two contrasting populations of seabuckthorn

2008· article· en· W2169618405 on OpenAlexvenueno aff
Gang Xu, Baoli Duan, Chunyang Li

Bibliographic record

VenueCanadian Journal of Forest Research · 2008
Typearticle
Languageen
FieldMedicine
TopicPhytochemical and Pharmacological Studies
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsAPXBiologyPopulationCatalaseDrought tolerancePhotosynthesisBotanyShootProlineHorticulturePoint of deliveryAgronomyAbscisic acidAntioxidant

Abstract

fetched live from OpenAlex

Two contrasting populations of seabuckthorn ( Hippophae rhamnoides L.) from western China were employed to study their differences in adaptive responses to drought. The Daofu population was from a wetter upland climate region, whereas the Dingxi populations was from a drier lowland climate region. A completely randomized design with two factors, two populations and two watering regimes (100% and 25% of the soil water holding capacity), was used. In both populations, drought significantly decreased growth and the net photosynthesis rate (A), and significantly increased the root/shoot ratio (RS), catalase (CAT), peroxidase (POD), glutathione peroxidase (GPX) and ascorbate peroxidase (APX) activities, and abscisic acid (ABA) and proline contents. Compared with the Daofu population, drought induced a greater RS value, higher CAT, GPX, and APX activities, and a higher ABA content in the Dingxi population, whereas the gas exchange traits, for example, the stomatal limitation value (L S ) and intercellular CO 2 concentration (C i ), were less responsive to drought in the Dingxi population. The two populations may have developed different strategies to tolerate drought, such as different pathways to dissipate excess absorbed light energy, to resist oxidative stress, and to keep water status. Such factors enable the Dingxi population to tolerate drought better than the Daofu population.

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)0.002
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 score0.219
Threshold uncertainty score0.247

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.243
GPT teacher head0.424
Teacher spread0.181 · 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

Citations9
Published2008
Admission routes1
Has abstractyes

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