Reducing the sensitivity of the water quality index to episodic events
Bibliographic record
Abstract
We demonstrated a general relationship between water quality index (WQI) values and indices of benthic community composition for a set of 32 streams from British Columbia and Ontario. Streams that produced lower WQI values tended to have benthic communities characteristic of degraded water quality. Streams, in contrast, that produced higher WQI scores tended to have a fauna characteristic of high water quality. Trimming the water quality data for high-total suspended solids (TSS) events increased the WQI values by as much as 30 points. There were modest but apparent increases in the strength of the association between the WQI and indices of benthic community composition when the water quality data records were trimmed of values that occurred during periods of high (extreme) turbidity. Trimming data that contained turbidity (or TSS) values beyond the mirrored 5th, 90th, or 95th percentile were about equal in their effect on the WQI. The removal of high TSS samples on the basis of the ‘mirror’ method can be recommended on the basis that it will likely correctly remove data that have a long right-hand tail, and will also correctly not remove data when the data are more normally distributed.
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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 | 0.004 | 0.020 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".