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Record W2025877187 · doi:10.1139/x04-020

Age estimation of <i>Quercus marilandica</i> and <i>Quercus stellata</i>: applications for interpreting stand dynamics

2004· article· en· W2025877187 on OpenAlexvenueno aff
Stacy L. Clark, Stephen W. Hallgren

Bibliographic record

VenueCanadian Journal of Forest Research · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicForest ecology and management
Canadian institutionsnot available
FundersOklahoma Agricultural Experiment StationNature ConservancyOklahoma State University
KeywordsPithCoringDiameter at breast heightMathematicsDendrochronologyTree (set theory)BotanyForestryBiologyHorticultureGeographyPaleontologyDrilling

Abstract

fetched live from OpenAlex

We compared methods to correct age of cores that failed to intercept the tree's pith for two oak species, blackjack oak (Quercus marilandica Muenchh.) and post oak (Quercus stellata Wangenh.), and determined the difference in age at tree base (0–10 cm from ground level) versus breast height (1.4 m from ground level). Methods to correct age of off-center cores were relatively similar in error for both species (2.9–8.1 years). Ocular estimation of number of rings to pith required the least amount of data collection and manipulation to apply. A regression technique using tree diameter provided the lowest absolute error (9–13 years) for age estimation of rotten cores, compared with methods that used mean ring widths to extrapolate tree age. Age difference due to coring height averaged 9 years for both species and was highly variable, indicating that trees should be cored as close to ground level as possible. Age structure of this forest could be accurately reconstructed to within 5-year age-classes using recommended methods, with the exclusion of large, rotten, and putatively old trees.

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)0.002
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 score0.017
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.014
GPT teacher head0.283
Teacher spread0.269 · 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

Citations43
Published2004
Admission routes1
Has abstractyes

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