A water quality assessment of Lake Manitoba, a large shallow lake in central Canada
Bibliographic record
Abstract
Water quality of Lake Manitoba is poorly understood in comparison to other large lakes and a study was undertaken to characterize the spatial and temporal variation in water quality. To characterize the lake wide water quality conditions, samples were collected from 15 stations over a 2 year period. Geospatial mapping and principal components analysis revealed that the south basin of the lake was more turbid, nutrient rich, and more dilute in comparison to the north basin. Water samples collected daily during the operation of the Assiniboine River Diversion in 2005 and 2006 indicated that the Assiniboine River Diversion was the single largest source of phosphorus and sediment and was the second largest source of nitrogen to the lake. A non-parametric trend analysis of a 17 year historical water quality dataset from a station in the south basin indicated that Lake Manitoba has become more dilute and nutrient rich over time.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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 teacher head, 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".