Do chestnut, northern red, and white oak germinant seedlings respond similarly to light treatments? Growth and biomass
Bibliographic record
Abstract
Northern red oak ( Quercus rubra L.) seedling growth has been extensively studied. White oak ( Quercus alba L.) and chestnut oak ( Quercus prinus L.), however, are far less investigated despite their importance among upland oak species in eastern North American forests. We characterized white and chestnut oak seedling response to light and available soil nutrients while using northern red oak as a benchmark. Germinants were grown within one of three shade treatments (25%, 18%, and 6% of full sun) in one of two native forest soil mixes over two growing seasons. Leaf area, shoot mass, and root mass of all three species showed positive growth responses to increasing light. Growth and biomass were higher for all species grown in the more nutrient-rich forest soil, but chestnut oak displayed the greatest positive responses to the higher nutrient levels. White oak seedlings were the slowest growers and demonstrated the most root-centered growth, with root to shoot ratios almost twice that of either chestnut or northern red oak seedlings. The oak species evaluated here responded differently to changes in resource availability. Our study demonstrates the differential response of upland oaks to low light and nutrients. These differences need consideration when developing oak management prescriptions for specific oaks.
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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".