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Record W1947766747 · doi:10.1139/cjb-2014-0129

Relationship between vessel porosity and leaf emergence pattern in ring- and diffuse-porous deciduous trees in a temperate hardwood forest

2014· article· en· W1947766747 on OpenAlexvenueno aff
Sayaka Takahashi, Naoki Okada, Tadashi Nobuchi

Bibliographic record

VenueBotany · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicPlant Water Relations and Carbon Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsDeciduousBiologyHardwoodBotanyTemperate deciduous forestPhenology

Abstract

fetched live from OpenAlex

To elucidate the functional relationship between intra-annual variations in vessel diameter and leaf emergence pattern of ring-porous and diffuse-porous deciduous trees in temperate forests, we determined the temporal relationships between leaf phenology and vessel formation. Cylindrical stem cores were periodically collected from each of five ring- and diffuse-porous species, and the leaf and vessel formation were observed simultaneously. In the ring-porous species, vessel formation began within 2 weeks of leaf appearance, and most wide vessels were formed within 2 weeks of full leaf expansion. More of the trees with intermediate-type formed medium-sized vessels or sparse wide vessels than trees with the flush-type leaf emergence, especially between full leaf expansion and the end of shoot elongation. Narrow vessel formation began 2–8 weeks after full leaf expansion in all specimens. The transition between the pore- and non-pore zones was abrupt in the flush-type species, and gradual in the intermediate-type species. In contrast, diffuse-porous species formed vessels 0–8 weeks after full leaf expansion. Our findings suggest that ring-porous species form leaves and vessels synchronously to accommodate water-transport requirements, whereas diffuse-porous species form these tissues asynchronously; thus, unlike the latter type, the former species change vessel diameters according to leaf formation.

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 score0.013
Threshold uncertainty score0.789

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 head0.013
GPT teacher head0.212
Teacher spread0.199 · 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

Citations28
Published2014
Admission routes1
Has abstractyes

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