The impacts of forest harvest and wildfire on soils and hydrology in temperate forests: A baseline to develop hypotheses for the Boreal Plain
Bibliographic record
Abstract
This component of the Forest Riparian and Watershed Disturbance (FORWARD) project evaluates soils in burned, harvested, and undisturbed watersheds to quantify effects of disturbance on forest soils in the Boreal Plain. The current knowledge of disturbance effects on soils is reviewed, with a focus on temperate ecosystems, to generate the following hypotheses to be tested in field studies. Harvest and fire should affect surface horizons more than mineral horizons. Forest harvest should affect soils most through compaction, the degree being mediated by soil texture, soil moisture, and harvest operations. Nutrient export will likely increase from logged watersheds, with changes in microbial processes being more strongly associated with nitrogen (N) losses and erosional processes more strongly linked to phosphorus (P) losses. Effects of fire on Boreal Plain soils will increase with fire severity. Coarser soils after fire should be subject to hydrophobicity, erosion, and low infiltration rates. Chemical changes should include increased solubility of oxidized cations. N losses should exceed P losses and be due to volatilization and leaching of mineralized N compounds. Particulate P should comprise P loss; although P availability may increase after fire, soluble P movement from the forest environment will be limited by P uptake by soils. Key words: watershed disturbance, forest soils, forest fire, forest harvest, soil properties, nutrient cycling.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".