Tree architecture as a predictor of growth and mortality after an episode of red oak decline in the Ozark Highlands of Missouri, U.S.A.
Bibliographic record
Abstract
Mixed oak stands in the Ozark Highlands of southern Missouri were revisited eight years after a severe episode of red oak decline to determine which predictor variables, collected in 2003, best predicted subsequent tree growth and mortality patterns. Between 2002 and 2009, the mortality rate was 5% (0.625% annual mortality rate), generally below previously reported background rates. Generalized linear mixed models indicated that dieback (an estimate of branch mortality), age, relative height, and the interaction between the last two were most effective at predicting tree mortality. By contrast, tree vigor index (TVI), a composite variable derived from basic measurements of crown and stem architecture, was unequivocally the best predictor of basal area growth trend from one long-term period to the next. Basal area growth increases linearly with TVI, reinforcing the notion that even in ring-porous oaks (which must build new earlywood vessels each year), sustained growth is a low priority for carbon allocation in chronically stressed trees. The findings validate TVI as a useful metric for predicting growth rates of scarlet oak (Quercus coccinea Münchh.) and black oak (Quercus velutina Lam.).
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".