Watershed Assessment of the Canaseraga Creek Watershed, Including Water Quality Analysis, SWAT Model, and Investigation of the Applicability of a Nutrient Biotic Index
Bibliographic record
Abstract
Nearshore areas of Lake Ontario are suffering from persistent water quality impairments that were generally not resolved through programs such as the phosphorus abatement program and the Great Lakes water quality agreement. A major nearshore area of concern is the Rochester Embayment, which receives the discharge of the Genesee River. Due to the predominance of agriculture in the Genesee River basin and its largest tributary, Canaseraga Creek, agricultural areas were investigated using the segment analysis sampling technique and Soil and Water Assessment Tool (SWAT) modeling. Individual nonpoint areas were identified as nutrient sources as well as seven wastewater treatment plants. In general, loadings increased moving downstream as more source areas such as concentrated animal feeding operations, wastewater treatment plants, and small agricultural operations contributed to the nutrient load. Two tributaries, Twomile and Buck Run creeks, generally had the highest average annual concentrations and areal loadings of nutrients due to concentrated animal feeding operations (CAFOs) and dominance of agriculture in those areas. Observed loading data was used to calibrate a SWAT model for Canaseraga Creek. The most effective agricultural management practice was grassed waterways, while upgrading wastewater treatment plants to better (tertiary) treatment was also effective. By targeting just the areas that contribute the most P (Buck Run Creek, Twomile Creek, Groveland Flats) with grassed waterways, upgrading WWTPs, and stabilizing erodible main-stem streambanks, total phosphorus (TP) concentration was reduced by 31.4% from 104.3 ?g P/L to 71.6 ?g P/L. Of the three considered potential TP water quality targets (20, 45, 65 ?g P/L), the 65 ?g P/L target was attainable, while the 45 ?g P/L standard was not achieved but is believed to be possible with more intensive management practices. A nutrient biotic index (NBI) using TP and nitrate concentrations with observed macroinvertebrate communities was also used to evaluate appropriate water quality criteria. When comparing trophic state from the NBI with an external classification scheme based on chemistry, the NBI-P trophic state designations were observed to agree more often than the NBI-N. Several reasons for the discrepancies were determined, namely the use of nitrate instead of TN for the NBI-N, number of chemistry samples used, period of time which chemistry averages were taken, tolerance values that may not completely represent nutrient 'optima', and lack of scores for many taxa.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".