Geomorphological and meteorological drivers of ephemeral pond hydrology in the Canadian shield forest
Bibliographic record
Abstract
Abstract Ephemeral ponds in cold and humid forested regions are often vulnerable because of their small size and ephemeral nature. The aim of this study was to identify the geomorphological and meteorological drivers of ephemeral pond hydrology in the forests of cold and humid climates. A multi‐year study of 40 ponds was conducted in the Kenauk forest located in the Canadian Shield of the Outaouais region (Quebec, Canada). Pond bathymetry, mineral and organic sediment thickness, watershed area, slope, canopy cover and maximum depth were measured in‐situ. The spring hydroperiod (the period during which there is water in the ponds between April and October) as well as the surface and groundwater recession rates were measured throughout the study period, from 2016 to 2020. Data from this study show that ponds located at lower altitudes in the landscape are larger, receive water from a larger area, and have longer hydroperiods than ponds located at higher altitudes. The results demonstrated a connection between the ponds and the surrounding aquifer. Spring and early summer precipitation was shown to affect hydroperiods the most, while summer precipitation influenced the presence of water in the ponds from April to October. Winter precipitation appear determinant for pond recession rates. Simple multiple regression models were able to simulate hydroperiods and hydroperiod indices relatively well, but pond recession rates were not well represented by the models. This study brings original multi‐year and multi‐site data identifying parameters and variables that determine ephemeral pond hydrology in cold and humid climates. The results provide new insights into their resilience on the landscape and bring new arguments for their long‐term protection.
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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 | 0.001 |
| 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 | 0.000 |
| 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".