Hydrologic response of a small forested swamp complex, Seymour Valley, British Columbia
Bibliographic record
Abstract
Wetlands are common in Coastal Western Hemlock forests yet the hydrologic processes that generate runoff from small swamps are not completely understood. Direct field observations, hydrologic, and electrical conductivity data were collected from a gently sloping forested swamp complex from July to November 2009. Swamps occupied depressions between raised mounds (0.1 to 3 m high) and were connected by an ephemeral creek. Runoff was controlled by antecedent moisture conditions and influenced by basin microtopography. Two hydrologic regimes occurred during the study period and different runoff processes dominated each regime. Runoff was generated by subsurface flow during dry antecedent conditions as swamps remained hydrologically disconnected from each other. Runoff was generated by surface outflow from hydrologically connected swamps during wet antecedent conditions as ponded water spilled out of the depressions. The forested swamp complex produced a faster but limited hydrologic response during dry antecedent conditions compared to a slower but greater hydrologic response during wet antecedent conditions. Stormwater runoff and runoff ratios were up to two orders of magnitude higher in wet conditions than during similarly sized events in dry conditions. These factors should be considered when designing monitoring programs or runoff models in forested swamps with significant microtopography. Field surveys and estimates of hydrologic inputs and outputs may be useful in predicting the potential hydrologic connectivity of isolated forested swamps.
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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) | 0.002 | 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".