Ten-year postharvest effects of silviculture systems on soil-resource availability and conifer nutrition in a northern temperate forest
Bibliographic record
Abstract
Silviculture systems (clear-cut, partial-cut, and unharvested forest) were compared 910 years after harvesting to determine their effects on conifer nutrition and the availability of soil resources, especially nitrogen. These results were used to discuss the effects of silviculture systems on tree growth in relation to the more commonly described effects of light. Differences in soil properties across the silviculture treatments were most apparent in the forest floor. Depth and C/N ratio of the forest floor had decreased slightly in clearcuts, and forest-floor moisture was highest under partial-cut forest. Despite these differences in soil chemistry and soil moisture, no differences were detected in mineralizable N (anaerobic incubation) or in situ net N mineralization among treatments. Height growth and foliar mass were reduced under the low-light conditions of the partial-cut forest, but there were no differences in foliar N concentrations of hybrid white spruce (Picea glauca (Moench) Voss × Picea sitchensis (Bong.) Carrière), western redcedar (Thuja plicata Dougl. ex D. Don), or western hemlock (Tsuga heterophylla (Raf.) Sarg.) saplings. Mature western hemlock trees in partial-cut forest also had concentrations of foliar N equal to that of mature trees in the unharvested forest. Overall, we detected only minor effects of silviculture systems on soils after 10 years, and we conclude that light availability is likely more responsible for the current differences in tree growth.
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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".