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Record W2053689703 · doi:10.1139/x07-184

The effect of age and sample position on eucalypt tree-ring width series

2008· article· en· W2053689703 on OpenAlexvenueno aff
Matthew Brookhouse, Cris Brack

Bibliographic record

VenueCanadian Journal of Forest Research · 2008
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTree-ring climate responses
Canadian institutionsnot available
Fundersnot available
KeywordsDendrochronologyPopulationBiologyDendroclimatologyTree (set theory)MathematicsDemographyCombinatorics

Abstract

fetched live from OpenAlex

In this study, we examine within-tree variance in proportionate tree-ring width (prw) and correlation between radii (rwt), samples (rbh), and trees (rbt) in four eucalypt species common throughout eastern Victoria, Australia. In all species and sites studied, rwt and rbh differ significantly between cambial-age and sample-height classes. Differences between rwt and rbh are nonsignificant between tree-age or radial-azimuth classes. Significant differences in prw exist between cambial-age, tree-age, and sample-height classes and between radii on different compass bearings. Whilst differences in rwt and rbh between cambial-age classes appears to be the result of variation in mechanical demands with increasing stem size, stem deformation may account for differences between sample-height classes. Variation in prw appears to be due to stem geometry, stem eccentricities associated with root buttressing, and tree-age dependent variation in the rate of radial growth. No significant differences in rbt exist between sample-height classes. Analysis of the expressed population signal statistic indicates that site-level and species differences are far more significant than sample position on variance within tree-ring width series collected from eucalypts. We discuss the implications of the current work for eucalypt dendroclimatology.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
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)0.0010.000

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 head0.037
GPT teacher head0.281
Teacher spread0.245 · 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 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

Citations15
Published2008
Admission routes1
Has abstractyes

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