Buffalo River Floatables Control and Continuous Water Quality Monitoring Demonstration Project
Bibliographic record
Abstract
A floatables trap was installed at the mouth of a combined sewer outfall in the BuffaloRiver,NY,toevaluatetheutilityofthistechnologyinreducingfloatables dispersal.In addition to the tloatables trap, two Hydrolab Datasondes were installed to demonstrate the importance of a continuous monitoring system in evaluating impacts of combined sewer overflows (CSOs) on receiving water quality.One Datasonde was hung in the river from the floatables trap near the mouth of the outfall and one was attached to an upstream bridge abutment, to represent ambient river conditions.Monitored parameters included dissolved oxygen, pH, conductivity, temperature, and redox.Floatables were collected successfully over an eight week period that included both dry weather and three CSO events.The dry weather floatables were collected and analyzed as representative of fugitive inputs from the river that were not related to CSO activity.The floatables were sorted into nine categories and the number, mass, and volume in each category were determined.Mean floatables accumulation rates in the trap were significantly greater for CSO periods than dry weather periods.The distribution of floatables, by category, in the sampled CSOs was compared to the distribution for two sewersheds from a study conducted in Newark, New Jersey.The distributions for the two New Jersey sewersheds were similar to each other, but the floatables in Buffalo consisted of more wood and less plastic.The average mass of tloatables trapped per 1,000 cubic feet (28.3 m 3 ) of CSO discharge was considerably less for the Buffalo sewershed than the averages for the two New Jersey sewersheds.Low tloatables discharge from the Buffalo sewershed, in part, may be due to sewer hoods.Irvine, K. 2002."Buffalo River Floatables Control and Continuous Water Quality Monitoring Demonstration Project."Journal of Water Management Modeling R208-l 0.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".