Overstory influences on herb and shrub communities in mature forests of western Washington, U.S.A.
Bibliographic record
Abstract
Understanding the relationships between forest overstory and understory communities is essential for predicting changes in the abundance and distribution of understory plants through successional time and in response to forest management. We used correlation analysis, multiple regression, and nonparametric models to explore the relationships between overstory characteristics (canopy cover, stand density, and tree-size distributions) and the abundance of species in the herb and shrub layers in mature forests of western Washington. Overstory variables explained >50% of the variation in the mean response of total shrub cover and ca. 50% of the variation in cover of Acer circinatum Pursh (the most common shrub species) and late-seral herbs (species reaching their greatest abundance in late-successional forests). Stronger relationships (80-90% variance explained) were found between overstory variables and the maximum cover of total shrubs, A. circinatum, total herbs, and each of three functional groups of herbaceous species. These empirical relationships represent both direct resource limitations and time-dependent responses for which overstory characteristics may be surrogates. Models of maximum abundance yielded the most consistent results, suggesting the relative importance of different overstory variables as limiting factors for understory response, although these limiting factors have different effects on plants with different life-history strategies.
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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 | 0.000 | 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) | 0.001 | 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".