Water Quality Assessment of Irondequoit Creek using Benthic Macroinvertebrates
Bibliographic record
Abstract
The Rochester Embayment of Lake Ontario is one the 43 Great Lakes' Areas of Concern designated by the Environmental Protection Agency (Monroe County 1993). As part of a Remedial Action Plan (RAP), degradation ofbenthos was one of the 14 use impairments identified for the Rochester Embayment (Monroe County 1993). Stage II of the RAP identified stream health monitoring as a method of identifying existing and future conditions of the Embayment and its tributaries, including Irondequoit Creek. There is much debate in the "world" of stream health biomonitoring using aquatic macro invertebrates regarding methods of collection, sample size and taxonomic resolution required to obtain accurate stream health assessments. My study compared stream health at three locations in Irondequoit Creek (upstream, midstream and downstream) and in three habitats (gravel, mud and vegetation) and evaluated methods of sampling macro invertebrates and analyzing stream health used by the Stream Biomonitoring Unit ofthe New York State Department ofEnvironmental Conservation (Bode et al. 1996). There were few differences between upstream (primarily agricultural or rural land use) and midstream (primarily agricultural and suburban l~d use) communities, but stream health decreased from upstream to downstream (primarily .urban/suburban land use). As expected, community differences were found across habitats (gravel, vegetation, mud) at the same sampling locations. Fixed 100 count · methods were compared with entire macro invertebrate samples in the gravel habitat at the midstream location (Powder Mill Park, Rochester, NY). Although metric values for random and haphazard samples of 100 organisms differed from values for whole samples, stream health assessments did not differ.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".