The effect of forest harvesting on streamflow recession curves at Carnation Creek, British Columbia
Bibliographic record
Abstract
At the Carnation Creek Experimental Watershed on southwestern Vancouver Island, British Columbia, the effect of harvesting, regeneration and road building were analyzed through the use of stream discharge data collected at a weir on the catchment outlet. The study was separated into a Pre-Logging period from 1971-1975, Logging from 1976-1981, and two Post-Logging periods from 1982-1985 and 1985-90 respectively. The current study focussed on the effects of harvesting on streamflow recession curves, which are an indicator of the ability of coastal watersheds to maintain low flows (base-flow) during the water-limited dry season. Approximately 30 years of discharge data along with rainfall and temperature were segmented into corresponding forestry operation periods. Using the linear relationship between log[|dQ/dt|] and log[Qm] according to storage-discharge theory, multiple linear models were created and a regression was used to investigate the significance of each of the forestry operations. It was found that the effect of the roads increased lateral slope interception of sub-surface flow and directed water along the ditch systems to the channel at a greater rate, steepening the recession curves at all discharge levels in the short term, but only persisting at low discharge levels. Harvesting increased the water table height, because of the reduction in transpiration via loss of interception, and low flows and total flows increased over both post-logging periods, which partially offset the effect the roads had on the recession. Regeneration began to occur over the harvested sections of the catchment and it was found their effect between post-logging periods was only significant with the inclusions of extremely low discharge levels. However, a second logging pass in 1987, removing 21% of the forested catchment in the headwaters is believed to have confounded the regeneration effects.
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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 | 0.002 |
| 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 | 0.001 |
| 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".