Bibliographic record
Abstract
Contaminated lake water is a large issue for all environmental factors such as the aquatic species and their ecosystems, contaminated lake water can also harm humans as most large lakes are the source for drinking water for cities around Canada.This study was conducted to understand the contamination in Loch Lomond (a Lake near Saint John, New Brunswick) which is used as a source for drinking water for the people in the city of Saint John and in the surrounding area.In this study, 21 different samples were chosen throughout different locations around Loch Lomond, seven sites were sampled, and each site was sampled at three different events.The water samples were collected in clean/sterile bottles and then delivered to Saint John Laboratory services where the analysis was conducted to determine the concentration of Orthophosphate, Total Phosphorus, Nitrate/Nitrite, Coliform, E-coli, Total Organic Carbon and Dissolved Carbon.The catchment area for each of the seven points was calculated based on the contour lines and topography.The runoff was estimated using the rain intensity data collected from the local weather station.Calculations were conducted for each contaminant to retrieve a value in mg/year.The results that displayed the most concerning values was the ortho-phosphate and total phosphate, this value ranged around 7x10-4mg/year and 10x10-4mg/year.Orthophosphate being detected in drinking water leads back to a common concern of leaking septic tanks; this is because Orthophosphate lines lead pipes in the sewage system and thus detecting it in drinking water means it is contaminated.These high values can lead to health issues like algal blooms that will harm both aquatic animals and humans, though this has not been seen in any serious health matters in the city.This study serves the purpose of informing locals, city professionals and those concerned with the quality of drinking water about the state of the Loch Lomond Lake and if it has or will lead to any major health issues.
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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 | 0.000 |
| 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".