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Record W1774770615 · doi:10.1139/cjb-2015-0065

Reliability of leaf relative water content (RWC) measurements after storage: consequences for in situ measurements

2015· article· en· W1774770615 on OpenAlexafffundvenue
Fallon M. Tanentzap, Alexandra Stempel, Peter Ryser

Bibliographic record

VenueBotany · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicPlant Water Relations and Carbon Dynamics
Canadian institutionsLaurentian University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsWater contentBiologyDesiccationHorticultureBotanyMoistureChemistry

Abstract

fetched live from OpenAlex

Relative water content (RWC) is widely used to describe plant water status, and is commonly measured gravimetrically. The ephemeral nature of leaf fresh mass poses severe constraints for such measurements in field-grown plants. These constraints can be overcome by transporting the leaves in waterproof containers into the lab. However, even then, leaves lose water, and other changes may happen. The effects of a delay on the measurement of RWC have not been quantified so far. In this study, the influence of duration of storage up to 96 h and storage temperature on RWC and its components was investigated for four species. Alnus incana subsp. rugosa (Du Roi) R.T. Clausen, Impatiens capensis Meerb., and Scirpus microcarpus J.Presl & C.Presl leaves were stored in plastic bags, whereas those of Comptonia peregrina (L.) J.M.Coult. were stored in plastic vials. RWC remained within 5% of the initial value during 24 h cool storage, but after that, larger changes were observed. The effects of storage were species specific, being most pronounced in species poorly protected against desiccation, and under warm conditions. The effects of storage were not only limited to water loss, but also included cellular degradation. In general, storage at 10 °C for 24 h enables measurement of RWC for field-grown plants with accuracy of a few percent, but care has to be taken with species vulnerable to desiccation, possibly requiring faster measurement and a cooler storage temperature.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.078
GPT teacher head0.250
Teacher spread0.172 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations75
Published2015
Admission routes3
Has abstractyes

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