Nursery cultural practices influence morphological and physiological aspen seedling traits: implications for post-fire restoration
Bibliographic record
Abstract
Aspen forests are threatened by the impacts of a changing climate and are showing large-scale mortality with meager natural regeneration to restore these loses. Therefore, there is an increasing demand for high-quality aspen seedlings to assist with forest restoration efforts. Nursery cultural practices can be used to alter aspen seedling traits to improve adaptability to dry planting conditions. In this study, the effects of container size (SC10 and D30; 158 and 490 mL, respectively) and nursery irrigation treatment (high and low irrigation; 90% and 70% container capacity, respectively) on seedling growth and a suite of morphological and physiological traits were investigated. The combination of large container size and low irrigation treatment resulted in seedlings with lowest height-to-diameter ratio and specific leaf area, which are desired traits for seedling performance on dry sites. Additionally, seedlings exposed to low irrigation conditions at the nursery stage had a lower (more negative) osmotic potential at full turgor, suggesting a higher likelihood of drought tolerance. Overall results from this study provide insight into utilizing nursery cultural practices to produce seedlings with target characteristics that may ultimately lead to establishment on harsh, dry planting sites in large-scale reforestation projects.
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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 | 0.001 | 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) | 0.001 | 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".