Soil retention, tree uptake, and tree resorption of<sup>15</sup>NH<sub>4</sub>NO<sub>3</sub>and NH<sub>4</sub><sup>15</sup>NO<sub>3</sub>applied to trembling and hybrid aspens at planting
Bibliographic record
Abstract
Many experiments conducted under controlled environmental conditions suggest that deciduous tree species are adapted to NO3rather than NH4+uptake. To test this under field conditions, we studied soil retention, tree uptake, and tree resorption of15N derived from either15NH4NO3or NH415NO3applied to trembling aspen (Populus tremuloides Michx.) and hybrid aspen (Populus tremula L. × P. tremuloides) at planting. Overall, the hybrid had greater dry-matter yield and took up more total N than the trembling aspen over two growing seasons after fertilization. The recovery of15N per tree was also higher for the hybrid aspen than for the trembling aspen. Trembling aspen showed higher resorption efficiency of foliage N before abscission than hybrid aspen; however, the total amount of N retranslocated before leaf senescence was the reverse because of higher foliar dry-matter yield of hybrid aspen. The higher recovery of15NH4+than15NO3by trees seemed to be governed by higher N retention potential of NH4+than NO3in the soil. These results suggest that fertilization strategies need to be made based on both N acquisition capability of species and site-related factors, such as soil pH and immobilizationmineralization potential.
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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".