Modeling the Subsurface Hydrology of Mer Bleue Bog
Bibliographic record
Abstract
In this study, the ecosys model was used to simulate the hydrology of the Mer Bleue bog, Ontario, Canada, with seasonally varying water tables in the upper 1 m. The soil profile was divided into three zones of peat (fibric, hemic, and sapric). In the model, large, readily drained macropore fractions in the fibric peat caused low water‐holding capacity and high infiltration rates, in contrast to hemic and sapric peat, with small macropore fractions, high water‐holding capacities, and low infiltration rates. Model results for peat water contents, θ, and water table depths, Z , were tested with continuous hourly measurements from 2000 to 2004 using time domain reflectometry probes and piezometers. Within the zone of pronounced water table variation, the θ and Z modeled with the Hagen–Poiseuille equation for macropore flow and Richards' equation for peat matrix flow corresponded better to the measured θ and Z (regression slopes between 0.62 and 1.03, intercepts between −0.05 and 0.02 m 3 m −3 , and R 2 between 0.40 and 0.56), than did the modeled θ and Z with Richards' equation alone (regression slopes between 0.33 and 1.43, intercepts between 0.11 and 0.22 m 3 m −3 , and R 2 between 0.27 and 0.41). The Richards equation alone, even when parameterized with extremely high or low bulk saturated hydraulic conductivities of fibric peat, modeled slower infiltration, greater water retention, and lower Z than measured. The implications of macropore flow might be of great importance for peatland hydrology, therefore this experimental and modeling work should be extended to other wetlands as well.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".