Tree growth response to fuel reduction treatments along a topographic moisture gradient in mixed-oak forests of Ohio, U.S.A.
Bibliographic record
Abstract
This study examined the effect of the soil moisture gradient on tree growth response to prescribed fire and thinning in oak-dominated forests of Ohio. Six hundred and ninety-six increment cores (348 trees, five species) were collected from eighty 0.1 ha plots distributed across four treatments (control, thin, burn, thin + burn) in two sites. Ring widths were converted to basal area increments (BAIs). A water balance approach based on geographic information systems (GIS) was used to assess the potential evapotranspiration (PET) and moisture deficit for each tree, along with a long-term integrated moisture index, also based on GIS. The moisture gradients varied considerably across the landscape, with the highest PET and moisture deficit on ridges and south-facing slopes. This variation influenced the BAI of the studied species, but more strongly in the control stands than in the managed stands, where treatment effects became the main drivers of growth. Oaks exhibited greater BAI on sites with intermediate moisture demand or stress, whereas the non-oaks had greater BAI on more mesic sites. Moisture deficit and PET also interacted to influence BAI of yellow-poplar (Liriodendron tulipifera L.) and white oak (Quercus alba L.), particularly in the control. These results demonstrate the strong regulatory effect of the topographically controlled soil moisture gradient on tree growth in mixed-oak (Quercus spp.) forests, which can be explored to better understand community response to prescribed fire and thinning treatments.
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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 | 0.000 |
| 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".