Landscape and weather controls on fine-scale calcareous fen hydrodynamics
Bibliographic record
Abstract
Calcareous fens are species-rich peatlands thought to form at discrete alkaline groundwater discharge points. Here the spatial and temporal variability in the peat pore-water hydrodynamics at a fine (plot) scale of three calcareous fens in southern Ontario was investigated over three growing seasons to evaluate the sensitivity of these wetlands to weather fluctuation and landscape position. Only a small area of the fens demonstrated patterns of groundwater upwelling, and positive vertical hydraulic gradients (VHG) were low, peaking at 0.1. Local decreases in saturated hydraulic conductivity generated areas of pore-water over-pressuring in the peat profile through much of the fens. Several areas were subjected to large negative VHG (max = −0.2), causing sustained groundwater recharge. In this study the strength of the connection to the principal source area of water (alkaline stream) determined the pattern and variability of calcareous fen peat hydrodynamics amongst three growing seasons differing markedly in precipitation. The range of pore-water hydrodynamics evident in this study provides evidence for the processes controlling the sensitivity of calcareous fens to climate and land-use change. A conceptual model linking calcareous fen landscape position to weather- and climate-induced hydrodynamic variability is presented to guide management of these biodiverse ecosystems.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".