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Record W4210356455 · doi:10.1139/cjb-2021-0218

Assessing intra-annual density fluctuations across and along white pine stems

2022· article· en· W4210356455 on OpenAlexvenueno aff
Elise W. Miller, Tim Rademacher, Patrick Fonti, Bijan Seyednasrollah, Andrew D. Richardson

Bibliographic record

VenueBotany · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTree-ring climate responses
Canadian institutionsnot available
FundersNatural Environment Research CouncilNational Science Foundation
KeywordsBiologySpatial distributionDendrochronologyPinus <genus>BotanyStatisticsPaleontologyMathematics

Abstract

fetched live from OpenAlex

Intra-annual density fluctuations (IADFs) are triggered by environmental cues, but whether they are distributed uniformly throughout the stem is not well documented. The spatial distribution of IADFs could help us understand variations in cambial sensitivity to environmental cues throughout the tree. We investigate how IADF distribution varies radially, longitudinally, and circumferentially within white pine (Pinus strobus L.) stems. We took wood samples at breast height, near branches, and at the top of the trees. We identified IADFs visually and measured their radial position within a ring as well as their circumferential arc in cross-sections. Intra-annual density fluctuations occurred in 22.2% of rings. The radial position of IADFs within a ring was remarkably consistent at roughly 80% of the total annual radial increment across heights, trees, and years of formation. The main factors affecting the likelihood of IADF occurrence were ring width, year of formation, and the interaction between the two. Being near branches or at the top of the tree slightly increased the probability of occurrence. Though the sample size was not large enough to provide conclusive results about the circumferential distribution of IADFs, our data suggest that the circumferential arc of the IADFs might be conserved throughout the stem.

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 score0.036
Threshold uncertainty score0.839

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 studies0.0010.000
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 head0.023
GPT teacher head0.271
Teacher spread0.249 · 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

Citations3
Published2022
Admission routes1
Has abstractyes

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