Assessment of the Integrity of Fish Communities In The Great Lakes Area: A Bayesian Perspective
Bibliographic record
Abstract
The Great Lakes are a well recognized national ecological feature and are a prized natural resource. As a basin as well as individual water bodies, the Great Lakes have undergone significant environmental changes. Fish communities have been impacted significantly by these environmental changes. As a result, the main aim of this thesis is to assess the overall state and integrity of fish communities across the Canadian portion of the Great Lakes. This was done by applying novel Bayesian modeling techniques to evaluate the spatio-temporal trends of mercury and polychlorinated biphenyls (PCBs), well-known global legacy contaminants, in several fish species across all of the Canadian Great Lakes. Another goal was to identify contamination “hot spots” and create consumption advisories across all four major water bodies. Furthermore, this study assesses the rate of tumour occurrence in areas of concern (AOCs) and proposes a cohesive framework to determine tumour rates across these heavily polluted regions. Lastly, this study makes use of the Hamilton Harbour as a case study to propose and implement a probabilistic framework which aims to determine the state of a system based on current data and knowledge that could be used towards environmental policy and management decisions. The main achievement of this research is to provide a very thorough interdisciplinary analysis of the aquatic environment of the Great Lakes, by evaluating the ecosystem’s history, chemistry and biology. The Bayesian methodology applied in this research accounts for environmental uncertainty. Rather than using uncertainty as an excuse to not give answers, the models implemented make use of uncertainty to formulate risks in an informative way that can be used in a management practice (fish contamination, fish tumours, eutrophication etc). The Great Lakes are interconnected to one another, while also being distinct ecosystem regions and although their current state and the state of the fish communities has improved significantly over the past forty years, further research and monitoring should be implemented to adapt and match changing future scenarios.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".