The Pothole Hydrology-Linked Systems Simulator (PHyLiSS): Development and Application of a Systems Model for Prairie-Pothole Wetlands
Bibliographic record
Abstract
The North American Prairie Pothole Region covers about 770,000 square kilometers of the United States and Canada (including parts of 5 States and 3 provinces: North Dakota, South Dakota, Montana, Minnesota, Iowa, Saskatchewan, Manitoba, and Alberta). The Laurentide Ice Sheet shaped the landscape of the region about 12,000 to 14,000 years ago. The retreat of the ice sheet left behind low-permeability glacial till and a landscape dotted with millions of depressions known today as prairie potholes. The wetlands that subsequently formed in these depressions, prairie-pothole wetlands, provide critical migratory-bird habitat and support dynamic aquatic communities. Extensive grasslands and productive agricultural systems surround these wetland ecosystems. In prairie-pothole wetlands, the compositions of plant, invertebrate, and vertebrate communities are highly dependent on hydrogeochemical conditions. Regional climate shifts between wet and dry periods affect the length of time that wetlands contain ponded surface water and the chemistry of that ponded water. Land-use change can exacerbate or reduce the effects of climate on wetland hydrology and water chemistry. A mechanistic understanding of the relation among climate, land use, hydrology, chemistry, and biota in prairie-pothole wetlands is needed to better understand the complex, and often interacting, effects of climate and land use on prairie-pothole wetland systems and to facilitate climate and land-use change adaptation efforts. The Pothole Hydrology-Linked Systems Simulator (PHyLiSS) model was developed to address this need. The model simulates water-surface elevation dynamics in prairie-pothole wetlands and quantifies changes in salinity. The PHyLiSS model is unique among other wetland models because it accommodates differing sizes and morphometries of wetland basins, is not dependent on a priori designations of wetland class, and allows for functional changes associated with dynamic shifts in ecohydrological states. The PHyLiSS model also has the capability to simulate wetland salinity, and potential future iterations will also simulate the effects of changing hydrology and geochemical conditions on biota. This report documents the development of the hydrological and geochemical components of the PHyLiSS model and provides example applications.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".