An investigation of roof runoff during rain events at the Royal Military College of Canada and potential discharge to Lake Ontario
Bibliographic record
Abstract
The Royal Military College of Canada, located on the north eastern shore of Lake Ontario, possesses an abundance of copper roofs and lacks surface water treatment prior to discharge into Lake Ontario. Rainwater, roof runoff and soil samples were collected and analyzed for copper and other parameters. Copper was consistently detected in runoff samples with average concentrations of 3200 ± 2100 μg/L. Multivariable linear regression analysis for a dependant copper runoff concentration yielded an adjusted R 2 value of 0.611, based on an independent variable model using minimum temperature, maximum temperature, total precipitation, and wind speed. Lake water samples taken in the vicinity of storm water outfalls draining areas with copper roofs ranged from 2.0 to 40 μg/L copper. Such data exceed the 2.0 μg/L Canadian Water Quality Guidelines for the Protection of Aquatic Life as outlined by the Canadian Council of Ministers of the Environment (CCME). Analysis of raw, filtered and digested forms suggested that the majority of copper present in runoff and lake water samples was in a dissolved form. The majority of soils taken in this study displayed copper concentrations below the 63 μg/g CCME residential/parkland land use limits. These findings suggested that ion exchange processes between runoff water and soil do not occur to a sufficient extent to elevate copper levels in soil. It may therefore be concluded that the eventual fate of copper, which is not discharged via storm water outfalls, is lost to the water table and Lake Ontario through the sub-soil.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".