Clearcut harvesting effects on soil and creek inorganic nitrogen in high elevation forests of southern interior British Columbia
Bibliographic record
Abstract
Responses of both soil and stream inorganic nitrogen (N) after operational clearcut harvesting were explored in two high elevation British Columbia Engelmann spruce-Subalpine fir (ESSF) forests. At one study site, net N mineralization was measured between 1 and 11 yr after harvest. At a second site, for 3 yr after harvesting, available soil ammonium and nitrate were measured using ion exchange resins, and drainage losses of N were estimated using tension lysimeters and soil water balance models. Stream water N concentrations were also measured in one unlogged, and two logged watersheds at the second study area for 12 yr, both pre- and post-clearcut harvesting. Growing season as well as over-winter increases in post-harvest soil nitrate were detected after 3 yr, and the increases were apparent for up to 11 yr. Seasonal losses of N in drainage were greater in clearcut soils than in the forest soils in year 2 for ammonium and in years 2 and 3 for nitrate. A high proportion of the N was lost in drainage during snowmelt. Stream water ammonium in all three streams and nitrate in the unlogged and one harvested creek remained at or below the detection level. In the third creek, the frequency of detection and concentrations of nitrate peaked 2 to 3 yr after 30 to 57% of the watershed was harvested. The annual peak in NO3-N concentrations occurred immediately before the snowmelt-driven stream flow peak. Drainage losses and stream N fluxes were both small, relative to the total soil N content. Key words: Nitrogen, N mineralization, nitrate, harvesting, leaching, stream N, snowmelt
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".