Statistical Properties of Hydrographs in Minerotrophic Fens and Small Lakes in Mid-Latitude Québec, Canada
Bibliographic record
Abstract
Minerotrophic fens cover a large proportion of the land in mid-latitude Québec. Since the last century, they have been subjected to an increase in mean water levels, which translates over a long period into an increase in the fraction of area covered by water-filled hollows, hypothetically slowly transforming them into shallow lakes as the hollows coalesce in larger ponds (aqualysis). This phenomenon progressively changes the hydrological reaction of aqualysed fens to rain events. Four sites (two fens and two shallow lakes), were monitored for rainfall, water table levels and surface runoff during two years in the La Grande River basin. Summer and fall hydrographs for rain generated events as well as relation between in situ water table and outlet surface runoff were compared, respectively via shape statistics and analyses of covariance. Depending on the hydrological property, results show some differences between sites, but not always systematically between fens and lakes. Fens had fewer runoff events than lakes but the events were of greater magnitude and duration. Four of the six hydrographs shape statistics (shape mean and variance, rising and falling slopes) were found to be significantly different between some sites, lakes (contrary to fens) being always in the same category. These results also indicate that the location and shape of individual ponds may play an important role in runoff generation. Concerning the relation between water table level and outlet runoff, regression slopes of fens were found to be steeper than those of lakes, especially in wet conditions. Climate change impact studies results suggest increases in annual runoff in this region in the future; this paper gives some insight about future hydrologic response of fens.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".