How do forest harvesting methods compare with wildfire? A case study of soil chemistry and tree nutrition in the boreal forest
Bibliographic record
Abstract
An important tenet of the natural disturbance paradigm as a basis for sustainable forest management is that impacts of interventions fall within the range of natural variation observed for the disturbance in question. We evaluated differences in soil nutrients, soil acid–base status, and tree nutrition between two harvesting methods (whole-tree (WTH) and stem-only (SOH)) and wildfire, 15–20 years after disturbance, to assess whether these harvesting methods have biogeochemical impacts that are within the natural range of variation caused by wildfires in boreal coniferous stands of Haute-Mauricie (Quebec). Both SOH and WTH created conditions of forest floor effective cation-exchange capacity, exchangeable Ca and K concentrations, base saturation, Ca:Al molar ratio, and organic C concentrations that were lower than the range of values for wildfires. We hypothesize that the immediate deposition of soluble base cations and the incorporation of recalcitrant organic matter that characterize wildfires generate biogeochemical conditions that are not emulated by either harvesting method. The improved soil nutritional environment after wildfire compared with SOH and WTH was reflected in jack pine ( Pinus banksiana Lamb.) foliar nutrient composition but not in black spruce ( Picea mariana (Mill.) BSP) foliage. The results raise uncertainties about the long-term base nutrient availability of the harvested sites on Boreal Shield 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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".