Effect of biofuel waste, urea, deer browsing, and vegetation control on <i>Pinus banksiana</i> seedling growth on a dry upland site in central Canada
Bibliographic record
Abstract
Ash from biofuels and nitrogen fertilizer are increasingly being used as soil amendments. While this can increase tree growth, it can also increase mammalian grazing and competition with vegetation. We applied moderate amounts of ash (1.5 t·ha−1·year−1) and urea (74 kg N·ha−1·year−1) in each of 2 years to a well-drained site in southeastern Manitoba, planted with Pinus banksiana Lamb. Subplots received deer browsing and (or) vegetation control. The ash resulted in an increase in pH in the upper 15 cm of mineral soil from ∼5.7 to 6.6, and the urea created short-term spikes in soil inorganic nitrogen (NH4 and NO3) levels. Urea combined with ash significantly increased seedling relative growth rates in the first 2 years, with seedlings being largest with urea, with or without ash. However, by the fourth year, seedling growth and size did not differ between the amendments. Urea application increased browsing damage to 91%, but only when vegetation was mowed. Browsing guards resulted in seedlings having 1.6 times greater shoot mass by the end of the fourth growing season. These results suggest that on sandy soils in the dry region of central Canada, P. banksiana may get little benefit from ash applications.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".