An expanded fish-based index of biotic integrity for Great Lakes coastal wetlands
Bibliographic record
Abstract
Biotic indicators are useful for assessing ecosystem health because the structure of resident communities generally reflects abiotic conditions integrated over time. We used fish data collected over 5 years for 470 Great Lakes coastal wetlands to develop multi-metric indices of biotic integrity (IBI). Sampling and IBI development were stratified by vegetation type within each wetland to account for differences in physical habitat. Metrics were evaluated against numerous indices of anthropogenic disturbance derived from water quality and surrounding land-cover variables. Separate datasets were used for IBI development and testing. IBIs were composed of 10-11 metrics for each of four vegetation types (bulrush, cattail, water lily, and submersed aquatic vegetation). Scores of all IBIs correlated well with disturbance indices using the development data, and the accuracy of our IBIs was validated using the testing data. Our fish IBIs can be used to prioritize wetland protection and restoration efforts across the Great Lakes basin. The IBIs will also be useful in monitoring programs mandated by the Agreement between Canada and the United States of America on Great Lakes Water Quality, such as for assessing Beneficial Use Impairments (BUIs) in Great Lakes Areas of Concern, and in other ecosystem management programs in Canada and the USA.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".