Applying the Ontario Lakeshore Capacity Model to Lakes in Nova Scotia
Bibliographic record
Abstract
The Ontario Lakeshore Capacity model (LCM) has been widely and successfully used for over 30 years. Water quality models such as the LCM are useful and important tools that can provide invaluable insights into past and future lake trophic status. This information can then be used to maintain water quality during future development, and to set realistic remediation goals. The LCM is simple and robust, making it an ideal candidate to study and test applications in different geographical regions. In this book, total phosphorus and volume-weighted hypolimnetic dissolved oxygen concentrations were modelled to identify changes in lake trophic status, shoreline development capacities and coldwater fish habitat. Preliminary sensitivity and uncertainty analyses were also conducted and outlined, so that proper result interpretation is possible; one of the most important aspects of scientific modelling. Through the research described in this book, the reader will not only understand the purpose and value of modelling, but should be able to comprehend the inner-workings of the LCM.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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; both teacher heads agree on what is shown here.
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".