Hypolimnetic upwelling in coastal embayments of Lake Ontario: Implications for restoration
Bibliographic record
Abstract
Coastal wetlands are an important ecosystem in the Great Lakes basin, providing spawning grounds and warm-water refuge for numerous fish and benthic invertebrate species during cold water upwelling events.Urbanization along the northwestern shore of Lake Ontario has led to a depletion of coastal wetlands, replacing them with artificial embayments.Three artificial embayments, the Credit River estuary, and one coastal marsh in Mississauga, ON were studied to determine if the artificial embayments function as warm-water refuge during upwelling events.Temperature loggers were placed in each study site and temperature was recorded every 15 minutes from July to October 2017.Upwelling events were isolated from the data, and frequency, magnitude, and duration of upwelling was determined.Most study sites had a frequency of 4 upwelling events throughout the study period.The average duration of upwellings varied from 30 to 70 hours, and the average temperature change ranged from -7.1ᵒC to -11.9ᵒC.All of the study sites seemed to buffer upwellings by reducing the magnitude of temperature change and increasing the duration of upwelling events to varying degrees.These results will inform the creation of future wetlands, restoration of existing embayments, and conservation of Great Lakes coastal wetlands.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 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 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".