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Record W2923249320 · doi:10.1111/pce.12997

Persistent negative temperature response of mesophyll conductance in red raspberry (<scp><i>Rubus idaeus</i></scp> L.) leaves under both high and low vapour pressure deficits: a role for abscisic acid?

2017· article· en· W2923249320 on OpenAlexafffund
Changpeng Qiu, Gilbert Éthier, Steeve Pépin, Pascal Dubé, Yves Desjardins, André Gosselin

Bibliographic record

VenuePlant Cell & Environment · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicPlant Water Relations and Carbon Dynamics
Canadian institutionsAgriculture and Agri-Food CanadaUniversité Laval
FundersNatural Sciences and Engineering Research Council of CanadaChina Scholarship Council
KeywordsRubusVapour Pressure DeficitAbscisic acidStomatal conductanceBlowing a raspberryDiurnal temperature variationHorticultureBotanyRosaceaeConductanceTranspirationEcophysiologyChemistryBiologyPhotosynthesisMeteorology

Abstract

fetched live from OpenAlex

Abstract The temperature dependence of mesophyll conductance ( g m ) was measured in well‐watered red raspberry ( Rubus idaeus L.) plants acclimated to leaf‐to‐air vapour pressure deficit (VPDL) daytime differentials of contrasting amplitude, keeping a fixed diurnal leaf temperature ( T leaf ) rise from 20 to 35 °C. Contrary to the great majority of g m temperature responses published to date, we found a pronounced reduction of g m with increasing T leaf irrespective of leaf chamber O 2 level and diurnal VPDL regime. Leaf hydraulic conductance was greatly enhanced during the warmer afternoon periods under both low (0.75 to 1.5 kPa) and high (0.75 to 3.5 kPa) diurnal VPDL regimes, unlike stomatal conductance ( g s ), which decreased in the afternoon. Consequently, the leaf water status remained largely isohydric throughout the day, and therefore cannot be evoked to explain the diurnal decrease of g m . However, the concerted diurnal reductions of g m and g s were well correlated with increases in leaf abscisic acid (ABA) content, thus suggesting that ABA can induce a significant depression of g m under favourable leaf water status. Our results challenge the view that the temperature dependence of g m can be explained solely from dynamic leaf anatomical adjustments and/or from the known thermodynamic properties of aqueous solutions and lipid membranes.​

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.626
Threshold uncertainty score1.000

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 head0.007
GPT teacher head0.182
Teacher spread0.175 · 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 designBench or experimental
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

Citations37
Published2017
Admission routes2
Has abstractyes

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