Predicting stump sprouting and competitive success of five oak species in southern Indiana
Bibliographic record
Abstract
We measured 2188 oak trees (Quercus spp.) on the Hoosier National Forest in southern Indiana before and 1, 5, and 10 years after clear-cutting to determine the influence of parent tree age, diameter breast height, and site index on the probability that there was one or more living sprouts per stump: (i) 1 year after clear-cutting (sprouting probability) or (ii) that were competitively successful 5 or 10 years after clear-cutting (competitive success probability). We used logistic regression to develop predictive models for five species in each of the three measurement years. Two species were in the white oak group: white oak (Quercus alba L.) and chestnut oak (Qurcus prinus L.). Three species were in the red oak group: black oak (Quercus velutina Lam.), scarlet oak (Quercus coccinea Muenchh.), and northern red oak (Quercus rubra L.). Black oak site index ranged from 15 to 25 m at an index age of 50 years on the study sites. Parent tree age and diameter at breast height were significant predictors in all models. Sprouting and competitive success probabilities decreased with increasing parent tree age and diameter at breast height. Increasing site index was a significant contributor of increasing sprouting probabilities for year 1 and competitive success probabilities for year 5. By year 10, site index was negatively related to competitive success for the white oaks but was not a significant predictor for the red oaks. The models have practical value for predicting the stump sprouting potential of oak stands in southern Indiana and possibly in ecologically similar regions.
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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 | 0.001 | 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 | 0.001 | 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".