Effect of variable-retention harvesting on soil nitrogen availability in boreal mixedwood forests
Bibliographic record
Abstract
Clear-cut harvesting of forests may be associated with increased availability and losses of nitrogen (N), and variable-retention (VR) harvesting has been proposed as an environmentally acceptable alternative to clear-cutting. In boreal forests, however, harvesting has often not led to significant increases in N availability and it is thus important to assess whether variable retention practices are necessary and justifiable in such forests with respect to nutrient dynamics. We compared N availability in clear-cut and VR-harvested stands in the boreal mixedwood forest of northwestern Alberta. We measured soil concentrations of nitrate, ammonium, soluble organic N, and microbial N in uncut, 50% and 20% retention, and clearcuts of deciduous-dominated, coniferous-dominated, and mixed stands 4 years after harvesting. There was little apparent effect of harvesting on N availability in all forest types. Nitrate, ammonium, and microbial N concentrations and net N mineralization and nitrification rates were similar in clearcuts and uncut forests and there was no threshold effect of harvesting on N availability. Soluble organic N concentrations were lower in coniferous and mixed clearcuts than in uncut stands on only one occasion. Clear-cut harvesting in itself does not appear to lead to long-lasting increased N availability and losses in boreal mixedwoods. Adoption of VR harvesting in these ecosystems may not be justified on the grounds of reducing changes in N availability when compared with clear-cutting.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".