Beyond fill and spill: Hydrological connectivity in a sub‐arctic bog‐fen‐tributary complex in the Hudson Bay Lowlands, Canada
Bibliographic record
Abstract
Abstract Patterned bog and fen peatlands, which dominate the landscape in the Hudson Bay Lowlands (HBL), act as important water storage and conveyance features in this region. In spite of their hydrological importance, there are currently no studies that define and characterize the thresholds of bog‐fen‐tributary hydrological connectivity in the HBL or their relation to seasonal and annual changes in water fluxes. To this end, hydrological (i.e., streamflow and groundwater levels) and meteorological (i.e., precipitation, snow depth, evapotranspiration, and temperature) data were collected at a 4.8 km2bog‐fen‐tributary complex between 2007 and 2018. Connectivity thresholds were best characterized into three states (disconnected, connected, and high activity) that incorporated 41%, 47%, and 12% of the study period and 4%, 18%, and 78% of runoff, respectively. Runoff generally peaked in the spring due to snowmelt, while connectivity was highest in the peatlands in the fall months when precipitation exceeded evapotranspiration due to cooling temperatures. Warmer than average spring temperatures accelerated snowmelt rate faster than frost table thaw rate in the fen; this reduced the amount of meltwater that entered storage, increased drainage from bog to fen, and decreased overall connectivity in the unfrozen season. Cooler than average spring temperatures delayed bog connection and ground thaw; the late frost melt provided a source of water to the bogs after melt into the late spring and early summer. This study provides a basis for the modelling of peatland hydrological connectivity in the region in the drier conditions anticipated with climatic warming and regional resource extraction.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".