4. Diatom‐based Paleolimnological Assessment of Long Term Water Quality Trends, Near Forrest Island, Lake of the Woods, Ontario
Bibliographic record
Abstract
Lake of the Woods (LOW) is a large, international freshwater body that shares borders with Ontario, Manitoba, and Minnesota. Previous studies from the LOW have found that water quality is spatially variable in this complex lake. The current perception is that cyanobacterial blooms have increased in frequency and intensity, generating much interest in determining whether increased nutrients have resulted in water quality deterioration. To address this concern, paleolimnological techniques will be used to examine changes in diatom assemblage over the last ca. 200 years on a dated sediment core retrieved near Forrest Island, close to the city of Kenora, Ontario. Comparisons will be made to other LOW sites that are elevated in total phosphorous (TP) and experience algal blooms (impact sites) as well as a site with low TP that does not experience algal blooms (reference site). Based on the Forrest Island diatom shifts, the following questions will be examined: (1) What is the baseline condition of this site? (2) Have diatom assemblages and/or water quality changed over time? (3) If so, are these changes comparable to other LOW sites?; and (4)What are the potential mechanisms for these changes? To aid our interpretation, a diatom‐based inference model for TP will be applied downcore to examine whether TP concentrations have changed over the last few centuries. Additionally, other mechanisms such as recent warming will also be examined. Results from this study could have important implications related to the impacts of multiple stressors on the LOW
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".