Influence of age on growth efficiency of <i>Tsuga canadensis</i> and <i>Picea rubens</i> trees in mixed-species, multiaged northern conifer stands
Bibliographic record
Abstract
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 VINCPLA 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".