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Evaluation of centrifugal methods for measuring xylem cavitation in conifers, diffuse‐ and ring‐porous angiosperms

2007· article· en· W2024726619 on OpenAlexaff
Yangyang Li, John S. Sperry, Haruhiko Taneda, S. E. Bush, Uwe G. Hacke

Bibliographic record

VenueNew Phytologist · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicPlant Water Relations and Carbon Dynamics
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsHydraulic conductivityConductivityCavitationXylemPorosityMaterials scienceDrop (telecommunication)Pressure dropAnalytical Chemistry (journal)ChemistryComposite materialSoil scienceChromatographyMechanicsBotanyGeologyBiologyPhysicsSoil water

Abstract

fetched live from OpenAlex

A centrifugal method is used to measure 'vulnerability curves' which show the loss of hydraulic conductivity in xylem by cavitation. Until recently, conductivity was measured between bouts of centrifugation using a gravity-induced head. Now, conductivity can be measured during centrifugation. This 'spin' method is faster than the 'gravity' technique, but correspondence between the two has not been evaluated. The two methods were compared on the same stem segments for two conifer, four diffuse-porous, and four ring-porous species. Only 17 of 60 conductivity measurements differed, with differences in the order of 10%. When different, the spin method gave higher conductivities at the beginning of the curve and lower at the end. Pressure at 50% loss of conductivity, and mean cavitation pressure, were the same in 14 of 20 comparisons. When different, the spin method averaged 0.32 MPa less negative. Ring-porous species showed a precipitous initial drop in conductivity by both techniques. This striking pattern was confirmed by the air-injection method and native embolism measurements. Close correspondence inspires confidence in both methods, each of which has unique advantages. The observation that ring-porous species operate at only a fraction of their potential conductivity at midday demands further study.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.488
Threshold uncertainty score0.276

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.045
GPT teacher head0.327
Teacher spread0.282 · 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

Citations89
Published2007
Admission routes1
Has abstractyes

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