Bibliographic record
Abstract
Muskrat Lake has recently been suffering from nutrient overloading due to unknown causes. The purpose of this investigation is to determine where excess nutrients are concentrated in the Muskrat Lake watershed, to monitor their levels over a six-week period, and to test the BioCord Reactor as a novel solution. The reactor is commonly used in man-made wastewater remediation systems to increase the level of biological treatment, and decrease concentrations of nitrogen and phosphorus. Collection bottles were rinsed three times at each collection site before samples were taken and shipped to the Canadian Nuclear Laboratories and the Ministry of Environment for analysis, or brought back to the lab. Lab equipment was purchased from HACH Industries, and the HACH TNT Kit 843 procedure was slightly modified to scan for an ultra-low level of phosphorus. Overall, the results suggest that agricultural runoff, the mixed wood forests, and the municipal wastewater treatment plant do not have significant impacts on the watershed, but more analysis is necessary before any definitive conclusions can be drawn. Furthermore, the BioCord Reactor was shown to be ineffective at the test site due to low rainfall, water depth, and water speed. More sites need to be analyzed for a longer period of time in order to determine if this technology is feasible as a short-term remediation tool for eutrophication.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".