Modelling aquatic vegetation and dissolved oxygen after a flood event in the Bow River, Alberta, Canada
Bibliographic record
Abstract
Traditionally, macrophyte density has been considered the primary factor affecting the large dissolved oxygen fluctuations in the Bow River. After a major flood event scoured macrophytes in 2005, and subsequently changed river dynamics, the City of Calgary needed to update their predictive computer model for water quality to reflect the new conditions, which led to this study. A 2006 aquatic vegetation survey was also completed to assess post-flood conditions. The survey found that the average macrophyte dry weight was much lower (28 g/m2 ± 100 (p = 0.05)) than the historic average of 241 g/m2 ± 29, while the average periphyton chlorophyll-a concentration was higher (343 mg/m2 ± 71) than the historic average (158 mg/m2 ± 17)). Dissolved oxygen (DO) fluctuations were similar to pre-flood levels despite changes in the dominant vegetation. Using the results of this survey, the significant and previously unrecognized effects of periphyton diurnal processes on DO concentrations in the Bow River were identified and the Bow River water quality model (BRWQM) was recalibrated to reflect these findings. Adjustments were made to the BRWQM’s periphyton submodel to account for the more dominant role played by these organisms in river processes, and a competitive shading factor between macrophytes and periphyton was also introduced to more accurately model the species' competition for available sunlight. This newly calibrated and validated version of BRWQM was tested and found capable of predicting the occurrence of low DO concentrations in the Bow River and can provide a useful tool for forecasting the water quality effects of the city's planned wastewater infrastructure expansion.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".