Applying a Bayesian Framework to Track Binational (Canada-USA) Loads and Sources
Bibliographic record
Abstract
The Red-Assiniboine River Basin (RARB) spans the Canada-USA border and discharges into Lake Winnipeg via the Red River. Recurrent harmful algal blooms caused by nutrient runoff coined Lake Winnipeg as “Canada’s sickest lake” and “most threatened in the World”. Invasive species such zebra mussels and spiny-water fleas have disrupted the aquatic ecosystem.SPAtially Referenced Regression On Watershed Attributes (SPARROW) is a watershed model that follows a stream network and relates water quality conditions to nutrient sources, landscape delivery factors, nutrient transport, and losses in streams and reservoirs/lakes. We ported components of the first binational SPARROW model (deterministic calibrations) for RARB to a Bayesian framework to account for the main sources of uncertainty: errors resulting from loading estimates, parametric uncertainty (incorporated with prior distributions based on literature values), and model structural error. Open-source languages were used: Python, R, and WinBUGS. The limits of the computational capacity of WinBUGS were tested, with a total of over 70,000 catchments across the RARB.We identified hot spots in Canada and USA at a sub-watershed scale, where wastewater treatment plants and agricultural inputs were the main contributors of Total Phosphorus (TP) to Lake Winnipeg. Within those hot spots, we looked at hot spots at the catchment scale, where wastewater treatment plants were contributing to nearly 100% of the TP loading.We will present various scenarios testing hypotheses on different TP sources, such as the inclusion of urban areas, the subdivision of selected source variables, and the variation of wastewater treatment plant loadings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".