Characterizing Plankton communities in Lake Ontario coastal wetlands along an urban land-use gradient
Bibliographic record
Abstract
This thesis presents the results of a study on the effect of habitat condition and water quality on plankton communities across an urban land-use gradient in the Lake Ontario coastal wetlands: Frenchman???s Bay, Lynde Marsh, McLaughlin Bay, and Bowmanville Marsh over two years (2018-2019). One of the study wetlands (McLaughlin Bay) was assessed over three years (2017-2019) for its suitability as a candidate wetland for biomanipulation restoration. I found water quality was generally not degraded along the urban gradient as expected. Nutrient rich waters and high chloride concentrations were determined to be important drivers of decreased diversity and higher algal biomass dominated by cyanobacteria. In my assessment of McLaughlin Bay, I found that due to the nutrient- and chloride-rich conditions, the plankton community was dominated by inedible algal communities, and small zooplankton taxa. These results do not support applying biomanipulation as a restoration approach in McLaughlin Bay at this time.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".