Application of Weather Radar in Estimation of Bulk Atmospheric Deposition of Total Phosphorus Over Lake Simcoe
Bibliographic record
Abstract
The decline of Lake Simcoe water quality has been attributed to high phosphorus inputs that result in excessive algae and macrophyte growth subsequently contributing to end-of-summer hypolimnetic dissolved oxygen depletion and loss of fish habitat. Out of the estimated 53 to 67 tonne/annum (1998 to 2004 water years) of phosphorus entering the lake, atmospheric deposition is believed to be responsible for 16 to 38 tonne/annum. Historical estimates for atmospheric deposition involved averaging rain gauge (rainfall depth) and rain quality (phosphorus concentration) station data. Through use of this procedure, any spatial variability in the data (quality and quantity) is lost as each gauge is given an equal weighting. This study proposes a methodology to use Next Generation Radar (NEXRAD) to spatially represent rainfall data and a method to correct radar-rainfall estimates to rainfall recorded by local rain gauges. From this analysis it was found that the radar generally represented localized rainfall well, with the majority of correlation coefficients (R2) being over 0.90. Radar related issues that resulted in poor R2 values included virga, overshooting beam, beam attenuation, range related issues and ground clutter. For large bulk atmospheric total phosphorus (TP) deposition events the dominant parameter in calculating TP loads was rainfall depth. Results from this analysis demonstrated a large (−88% to +44%) difference between historical and revised estimates of bulk atmospheric deposition of phosphorus over Lake Simcoe.
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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 | 0.000 | 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) | 0.000 | 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".