Long-term response of forest plantation productivity and soils to a single application of municipal biosolids
Bibliographic record
Abstract
Ouimet, R., Pion, A.-P. and Hébert, M. 2015. Long-term response of forest plantation productivity and soils to a single application of municipal biosolids. Can. J. Soil Sci. 95: 187-199. After 16 to 19 yr, we revisited four experimental trials set up in the early 1990s to evaluate the long-term impact of municipal biosolids applied in forest plantations. Tree growth and the soil were sampled to determine the effects of a single application of biosolids applied at (liquid equivalent) rates of 0, 130, 200, and 400 m3 ha-1. Tree radial growth responded markedly to biosolids in the young plantations, increasing from 18 % for Pinus resinosa to 62 % for Picea glauca, and up to 700 % for Quercus sp. Increases in phosphorus (P) concentrations in the tree foliage in response to biosolids could still be detected in the conifer trials. In the top 0-5 cm soil layer, organic carbon (C), total nitrogen (N), P, and copper (Cu) concentrations and pools increased, while soil compaction and bulk density decreased. In the deepest soil layer sampled (20-40 cm depth), the total N and calcium (Ca) pools were reduced by the biosolids treatments, while the pool of exchangeable acidity increased. Our observations indicate that a single application of liquid biosolids up to 400 m3 ha-1 (30 t ha-1 DM) in young forest plantations is a sustainable practice without undue risk to such podzolic soils.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".