A Surface Area-Volume Relationship for Prairie Wetlands in the Upper Assiniboine River Basin, Saskatchewan
Bibliographic record
Abstract
The proliferation of geographical information system applications in hydrology and spatially distributed hydrologic models, all using areal input data, has created a need for a simple technique for estimating the storage volumes of wetland areas. Prompted by a hydrologic basin study in support of a Federal/Provincial study of the upper Assiniboine River basin in Saskatchewan, surface area-volume data sets for 177 wetland sites were analysed using regression analysis techniques. A composite relationship was derived, made up of two regression formulae, one for use with wetland areas under 70 hectares and a second for wetland areas between 70 and 500 hectares. A comparison was made between the composite area-volume relationship resulting from this study and similar area-volume relationships derived for two Iowa river basins in 1967. Although the two Iowa regression formulae compare reasonably well with the results of this study for wetland areas under six hectares, volumes derived by the three relationships diverge rapidly for areas greater than six hectares. Further analyses of wetland area-volume relationships using data from other areas of the prairie region could lead to the development of a suite of formulae which could eventually be consolidated and distributed into regional zones of application for use by the water resource community.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".