Is the use of trees with superior growth a threat to soil nutrient availability? A case study with Norway spruce
Bibliographic record
Abstract
The renewed interest in the use of fast-growing tree species is accompanied by concerns about the adverse effects that these trees may have on soil. Four Norway spruce (Picea abies (L.) Karst.) provenance trials in Quebec were used to test the hypothesis that a more vigorous growth would not occur at the expense of marginalizing available nutrient pools. On these sites, the provenance showing the greatest overall productivity (high treatment) and the one showing the lowest productivity (low treatment) were studied. The divergence in total aboveground nutrient contents between the high and low treatments was high in all sites (i.e., 161%209%). Increased nutrient immobilization in trees did not cause any significant soil depletion of available base cations or total N at any site. Moreover, exchangeable Ca concentrations, cation-exchange capacity, and exchangeable Ca pools in the forest floor were significantly higher in the high treatment. It is concluded that in the short term, increased nutrient immobilization in trees does not create an apparent depletion of available base cations, perhaps because of a stimulation of soil mineral weathering and (or) a better retention of nutrients by the trees. Also, an effort to simulate mineral weathering using PROFILE showed the need for model improvement for applications at the plot level.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".