Runoff and inorganic nitrogen export from Boreal Plain watersheds six years after wildfire and one year after harvest
Bibliographic record
Abstract
This investigation in the Swan Hills, Alberta, located on the Canadian Boreal Plain, examined May through October runoff before, during and six years after wildfire in a 4th order watershed, compared to a 3rd order reference watershed. It also examined runoff and inorganic nitrogen flow-weighted mean concentration and areal export for 1 year after winter harvest in four 1st order watersheds compared to five reference 1st and 2nd order watersheds. Runoff and areal exports were normalized to precipitation at each site. Runoff impact ratios (year 1 post-disturbance value divided by the pre-disturbance value) for burned and harvested watersheds were 60 and 70% higher, respectively, than reference watersheds (P = 0.06). Runoff from the burned watershed remained elevated 6 years after fire. A trend for higher nitrate and ammonium concentrations, combined with higher runoff yielded impact ratios for areal ammonium and nitrate exports that were 130 and 170% higher, respectively, in harvested than reference watersheds (P = 0.08 for both). The proportion of the watershed harvested was positively related to runoff (r2 = 0.94, P = 0.03) and ammonium impact ratios (r2 = 0.96, P = 0.02), but not nitrate impact ratios (P = 0.30). Areal nitrate export in snowmelt was low in harvested watersheds compared to their pre-harvest condition and to reference watersheds.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".