Survival and growth of tree seedlings on reclaimed oil sands site in response to fertilizer and ground cover grass species
Bibliographic record
Abstract
Every year, oil sands mining results in a significant area of degraded land which needs to be reclaimed. In reconstructed mine sites, ground cover is helpful to minimize soil erosion by stabilizing soil. Besides soil erosion control, ground cover may also influence the establishment success of tree seedlings by affecting soil nutrient and moisture availability. Barley (Hordeum vulgare L.) and oats (Avena sativa) are the ground cover species being recommended for oil sands reclamation operations, but interactions of the ground cover with planted tree seedlings are not fully understood. The objective of this study was to evaluate the effect of different fertilizer rates on survival and growth of trembling aspen (Populus tremuloides) and white spruce (Picea glauca) tree seedlings planted with two cover crops (barley and oats). To fulfill the objective a greenhouse bioassay experiment and a field experiment were conducted. Under greenhouse conditions, soil moisture significantly increased height, root collar diameter (RCD) and biomass yield of tree seedlings. Fertilizer application significantly increased height and RCD in greenhouse, but not under field conditions. Even without ground cover competition, tree seedlings responded poorly to NPK fertilizer. Furthermore, survival rates of tree seedlings were significantly decreased with increased fertilizer application rates, and no positive growth and yield responses were observed in the field. Vigorous growth of ground cover species with high fertilizer application rates largely controlled survival and growth responses of tree seedlings. Trembling aspen was more sensitive to ground cover competition and negatively affected by barley and oats with added fertilizer, whereas white spruce was unaffected. Therefore, adding fertilizer to these systems appears to be of little benefit to early establishment and growth of tree seedlings as well as for successful reclamation.
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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".