Bibliographic record
Abstract
The Forest Watershed & Riparian Disturbance Project (FORWARD) was initiated in 2001 to study hydrologic and water quality impacts and recovery following watershed disturbance in the Boreal Forest. Now in its third phase, FORWARD continues to assess long-term recovery following forest harvest and fire and has extended research to recovery of reclaimed oil sands mine sites. Numerical models developed in the first phases are now being applied to the determination of watershed load and contaminant fate from the mine sites. The development of reclaimed and engineered soils, the success of various vegetation complexes, and the risk of toxicity and impacts to bio-indicators are being compared to the findings from the decade of continuous data collected during FORWARD 1 & 2 that sets our expectations for watershed recovery. The previous results indicate that runoff coefficients were strongly correlated with disturbance intensity with recovery for many key indicators (e.g. nutrient loading) occurring over three to six years. In the case of harvesting, no detectable changes were observed below 50% harvest intensity, and wetlands played a crucial role in mitigating hydrologic and water quality impacts obscuring the role of riparian buffers in this same function. The data collected by the FORWARD Project has been used to improve forest management practices and improve SWAT runoff modeling in the boreal forest, which can be used in forest management planning. Specific results from the first phase of FORWARD are outlined in this summary.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".