Development and use of the Wetland Fish Index to assess the quality of coastal wetlands in the Laurentian Great Lakes
Bibliographic record
Abstract
We use fish and environmental data from 40 wetlands of the Laurentian Great Lakes to develop the Wetland Fish Index (WFI), a tool that can be used to assess the quality of coastal marshes. A partial canonical correspondence analysis was used to ordinate fish species along multidimensional environmental axes that accounted for anthropogenic disturbance based on temperature, conductivity, and the presence of pollutants (e.g., suspended solids and primary nutrients). Compared with other measures of fish habitat quality (e.g., ShannonWiener diversity index and species richness), the WFI was the only index that was significantly related to the degree of water quality degradation and wetlands condition, as indicated by an independent index of wetland quality, the Water Quality Index (WQI). WQI ranks sites according to deterioration in water quality and is statistically related to the degree of land-use alteration in wetland watersheds. We demonstrate the usefulness of the WFI for detecting intrawetland variation between two sites in a degraded urban wetland, Frenchman's Bay, Lake Ontario, and to distinguish the heavily impacted wetlands in lower Green Bay from the less-impacted marshes in middle and upper Green Bay, Lake Michigan. This was accomplished by using only published fish data without corresponding environmental variables.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".