Drought-Conditioning of Quaking Aspen (Populus tremuloides Michx.) Seedlings During Nursery Production Modifies Seedling Anatomy and Physiology
Bibliographic record
Abstract
In the western US, quaking aspen (Populus tremuloides Michx.) regenerates primarily by root suckers after disturbances such as low to moderate severity fires. Planting aspen seedlings grown from seed may provide a mechanism to improve restoration success and genetic diversity on severely disturbed sites. However, few studies have examined the use of container-grown aspen seedlings for restoration purposes from both the outplanting and nursery production perspective. Thus, the purpose of this novel study was to examine how alterations in irrigation levels during nursery production across three seed sources would impact seedling performance attributes on harsh, dry outplanting sites. Irrigation treatments were based on three irrigation levels, determined gravimetrically: High = 90%, Medium = 80%, and Low = 70% of container capacity. The three seed sources represented a latitudinal gradient across the aspen range (New Mexico, Utah, and Alberta). Carbon isotope analysis indicated irrigation treatments were effective in creating higher levels of water stress for both the Low and Medium irrigation levels compared to seedlings under the High irrigation level. Seedlings subject to the Low irrigation level were found to induce greater height, higher photosynthetic rates, larger percentages of hydraulically active xylem, and faster xylem flow velocities compared to the High irrigation level. The lack of an interaction between irrigation treatments and seed source for nearly all response variables suggests that nursery conditioning via irrigation limitations may be effective for a range of aspen seed sources.
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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) | 0.000 | 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".