Impacts of acid deposition at Plastic Lake: forecasting chemical recovery using a Bayesian calibration and uncertainty propagation approach
Bibliographic record
Abstract
Given the importance of models in the development of environmental polices it is necessary to assess the uncertainty introduced by model parameterisation and its impact on predictions. In the current study, an uncertainty framework designed to perform automated calibrations and developed for use with the Model of Acidification of Groundwater in Catchments (MAGIC) was applied to Plastic Lake, a long-term study site in Southern Ontario, Canada. The primary objectives were to investigate the chemical response of soil and surface water at Plastic Lake to proposed acid (sulfur and nitrogen) emissions and assess the use of the framework at a regional level. Despite the relatively high amount of uncertainty associated with many of the model parameters, calibration resulted in relatively narrow parameter convergence. The importance of time-series stream data was clearly evident, with uncertainty decreasing with more observation years. The forecast improvements in stream Acid Neutralizing Capacity at Plastic Lake from–40 μeq/L in 1988 to 14 μeq/L in 2060 had 5 and 95% confidence bounds of–3 and 29 μeq/L, respectively. Despite the limited availability of soil chemical data in Ontario, the approach applied at Plastic Lake is viable on a regional basis given the abundance of water chemistry data.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".