Hydrodynamic modelling of Delta Marsh and simplified methods of discharge estimation for discontinuous inland coastal wetlands
Bibliographic record
Abstract
This thesis details the hydrodynamic research conducted at Delta Marsh as part of the Restoring the Tradition marsh rehabilitation project. Research has indicated that the hydraulic and hydrologic controls on the marsh can have considerable impacts on its ecological function, but the impacts of these controls had not previously been studied. Field hydrography and two-dimensional numerical modelling (using MIKE 21) provided insight into many aspects of the physical behaviour of Delta Marsh. Eighty five percent of the inflow to Delta Marsh from Lake Manitoba passes through Clandeboye Channel, and these discharge signals propagate as far west as Cadham Bay. Inflow to the marsh disperses quickly, and accounts for a small fraction of the water that exits the marsh during subsequent outflow. Thus, Portage Diversion water that enters the marsh through the lake can remain there even if there is a net loss in marsh volume over the season. Wind friction across Lake Manitoba has the greatest impact on short-term fluctuations in marsh volume and on the composition of marsh water, followed by the Portage Diversion and the natural inflows to Lake Manitoba. Expansions to flood diversion infrastructure will considerably impact the composition of Delta Marsh waters. Three methods of wetland discharge estimation were developed and tested. The most promising method was the regressed slope Manning method (RSMM), which estimates two-directional channel discharge as a function of the water surface elevations at both ends of a channel. When used in conjunction with the velocity index method, the RSMM can multiply the amount of reliable discharge data collected per research dollar. Thanks to its simple formulation, the RSMM is likely applicable outside of wetland settings, 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 | 0.002 |
| 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 | 0.001 |
| 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".