Predicting Dissolved Oxygen Concentration in Urban Watersheds: A Comparison of Fuzzy Number Based and Bayesian Data-Driven Approaches
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Dissolved Oxygen (DO) concentration is significantly and adversely impacted by urbanisation. However, the processes that govern DO concentration in riverine environments are complex, difficult to understand and to model. In the Bow River in Calgary, AB, Canada there is a need to predict DO concentration as part of the overall goal to improve river health for downstream users. In this research three data-driven models are used to predict DO concentration in the Bow River are compared. Fuzzy linear regression performed better than both simple linear regression and Bayesian linear regression. The fuzzy number based model was better able to capture the daily variability in DO in the Bow River and substantially improved low DO predictions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it