Logging impacts on the biogeochemistry of boreal forest soils and nutrient export to aquatic systems: A review
Bibliographic record
Abstract
Logging disturbances in boreal forest watersheds can alter biogeochemical processes in soils by changing forest composition, plant uptake rates, soil conditions, moisture and temperature regimes, soil microbial activity, and water fluxes. In general, these changes have often led to short-term increases in soil nutrient availability followed by increased mobility and losses by leaching to receiving waters. Among the studies we reviewed, dissolved organic carbon (DOC) exports usually increased after logging, and nitrogen (N) mineralization and nitrification often increased with resulting increased N availability and exports to receiving waters. Similar processes and responses occurred for phosphorus (P), but to a lesser extent than for N. In most cases, base cations were released and exported to receiving waters after logging. Several studies demonstrated that stem-only or partial-harvest logging reduced the impacts on nutrient release and exports in comparison to whole-tree clear-cutting. Despite these logging-induced increases in soil nutrient availability and movement to receiving waters, most studies reported little or no change in soil chemical properties. However, responses to logging were highly variable and often site specific. The likelihood, extent and magnitude of logging impacts on soil nutrient cycling and exports in boreal forest watersheds will be dependent on soil types, stand and site conditions, hydrological connectivity, post-logging weather patterns, and type and timing of harvest activities. Additionally, logging impacts can interact with, and be confounded by, atmospheric pollutant deposition and climate change. Further watershed-level empirical studies and modeling efforts are required to elucidate these interactions, to improve predictive capabilities, and to advance forest management guidelines for sustaining forest soil productivity and limiting nutrient exports.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".