Water Quality Assessment of Irondequoit Creek using Benthic Macroinvertebrates
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 it