Towards a Structural and Methodological Improvement of Eutrophication Modelling
Bibliographic record
Abstract
The credibility of the scientific methodology of mathematical models and their adequacy to form the basis of public policy decisions has frequently been challenged. Skeptical views of the scientific value of modelling argue that there is no true model of an ecological system, but rather several adequate descriptions of different conceptual basis and structure. The purpose of this work was to first advance the Bayesian calibration of process-based models for guiding the water quality criteria setting process in Hamilton Harbour, Ontario, Canada. The analysis suggests that the water quality targets for total phosphorus and chlorophyll a concentrations will likely be met, if the recommendation for phosphorus loading at the level of 142 kg day-1 is achieved. My dissertation also examines how the Bayesian approach can effectively support the decision making process by synthesizing the predictions of different models developed for the same system. The model averaging approach consolidates the finding that the existing total phosphorus goal is most likely unattainable. The discrepancy between the chlorophyll a predictions of the two models pinpoints the need to delve into the dynamics of phosphorus in the sediment-water column interface. This work also aims to examine statistical formulations that explicitly accommodate the covariance among the process error terms for various model endpoints. The analysis suggests that the statistical characterization of the model error can be influential to the inference drawn by a modelling exercise. Finally, my dissertation challenges the capacity of the ecological foundation of eutrophication models to predict the role of nutrient regeneration. It shows that the recycled nutrients can be significant drivers in low as well as in high-productivity ecosystems depending on the period of the year examined. My dissertation also discusses several prescriptive guidelines that should be helpful towards a structural and methodological improvement of eutrophication modelling.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 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 teacher head, 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".