Fertiliser Addition Is Important for Tree Growth on Cut-Over Peatlands in Eastern Canada
Bibliographic record
Abstract
Fertilisation is considered essential for tree growth on cut-over peatlands. However, little research has been carried out on these managed ecosystems in North America. Two experiments were conducted on peatlands planted with black spruce (Picea mariana (Mill B.S.P.) and tamarack (Larix laricina (Du Roi) K. Koch). The first experiment compared the efficiency of six different localised and soil-incorporated fertilisers, applied at planting time, in promoting the growth and survival of seedlings. A second experiment evaluated the nutritional needs of previously established black spruce and tamarack plantations that exhibited stagnating growth. Growth and survival of black spruce seedlings were best improved with the commercial Forest PakTM fertiliser (2N-0.5P-0.7K g per plant), whilst for tamarack the optimum was reached with an experimental formulation fertiliser (7N-3P-5K g per plant). Spot fertilisation with granulated PK fertiliser (0N-3.1P-5.7K g per plant) led to lower success for both species. For re-fertilisation, the shortage of phosphorus was the most growth-limiting factor for both tree species. Tamarack showed a beneficial response to a complementary application of potassium, whereas for black spruce the application of nitrogen and potassium in addition to phosphorous induced an additional growth increase.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".