Environmental Risk Assessment and Adaptive Management Implementation in Lake Simcoe, Ontario
Bibliographic record
Abstract
Addressing the problems of low deep-water oxygen concentrations and impairment of cold-water fish habitats in Lake Simcoe as a case study of a dimictic mesotrophic lake requires reduction of external phosphorus (P) loading. However, the efficiency of restoration efforts can be hindered by persistent internal P loading. This thesis develops a series of ecological and biogeochemical models, aiming at advancing our understanding of internal P recycling mechanisms in mesotrophic dimictic lakes. Special emphasis is given to sediment diagenesis processes and their interplay with the water column, macrophyte-mediated P retention, and the nutrient nearshore shunt induced by dreissenids. First, a continuous Bayesian network is presented to investigate the cause-effect relationships among physical conditions, ambient nutrient concentrations, and plankton dynamics. P sediment internal loading is subsequently quantified with a reactive-transport simulation model of the transformation of P binding forms. Sediment dynamics are then assessed under conditions of varying organic matter sedimentation and hypolimnetic oxygen levels. Finally, an integrated P mass-balance model is used to elucidate the internal P fluxes stemming from sediments, macrophytes and dreissenids. The model predicts that P diffusive fluxes from the sediments account for less than 30-35% of the exogenous P loading in Lake Simcoe. In the post-dreissenid invasion era, the limited decrease of the ice-free TP concentrations is indicative of the presence of active nutrient recycling pathways, potentially magnified by the particular morphological features and hydrodynamic patterns of Lake Simcoe, which counterbalance the direct effects of dreissenid filtration.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".