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Record W2114098051 · doi:10.1139/x02-120

Influence of age on growth efficiency of <i>Tsuga canadensis</i> and <i>Picea rubens</i> trees in mixed-species, multiaged northern conifer stands

2002· article· en· W2114098051 on OpenAlexvenueno aff
Robert S. Seymour, Laura S. Kenefic

Bibliographic record

VenueCanadian Journal of Forest Research · 2002
Typearticle
Languageen
FieldEnvironmental Science
TopicForest ecology and management
Canadian institutionsnot available
Fundersnot available
KeywordsTsugaCanopySite indexBiomass (ecology)ForestryBiologyBotanyEcologyGeography

Abstract

fetched live from OpenAlex

Well-known patterns in the fundamental relationship between tree-level stemwood volume increment (VINC) and projected leaf area (PLA) are examined and quantified for Tsuga canadensis (L.) Carrière (eastern hemlock) and Picea rubens Sarg. (red spruce) growing in managed, mixed-species, multiaged stands in east-central Maine, U.S.A. Both species follow a sigmoid pattern, suggesting a peak growth efficiency (GE, stemwood volume growth per unit of PLA) in mid- to upper-canopy trees with PLAs of less than half that of the largest trees sampled. Tree age negatively influenced the VINC–PLA relationship in the expected manner: at a given PLA, older trees produce less stemwood than younger ones. The combined effect of leaf area and age is accurately modeled with a Weibull-like function in which the asymptote is an index of tree maturity defined as tree age relative to an estimated maximum for the species. Although previous studies have independently documented both the sigmoid relationship between VINC and PLA and the negative effect of age, their conclusions have been confounded by the strong correlation between age and mean tree PLA. This study addresses both issues simultaneously, and is the first to demonstrate a decline in GE with age independent from the effect of increasing PLA.

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)0.001
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 score0.862
Threshold uncertainty score0.924

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.020
GPT teacher head0.235
Teacher spread0.215 · 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

Citations72
Published2002
Admission routes1
Has abstractyes

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