Influence of water-level disturbances on the performance of ecological indices for assessing human disturbance: A case study of Georgian Bay coastal wetlands
Bibliographic record
Abstract
In this study, we compare the performance of three ecological indicators (Water Quality Index (WQI), Wetland Macrophyte Index (WMI) and Wetland Fish Index (WFI)), to assess the impact of human activities on ecosystem health of coastal marshes in eastern and northern Georgian Bay (Lake Huron) over two decades (1999–2019), when there had been a minor change in human population (increase of 7%), but a marked difference in the pattern of water-level fluctuations. Lake Huron-Michigan is known to have 8 and 12-year oscillations in water levels, but between 1999 and 2019, water levels remained 0.5 m below the long-term mean for 14 years, and then abruptly rose nearly 1 m, remaining high for the next five years. We compared index scores of wetlands surveyed during 2003–2013 (Period 1; low-water years) with those surveyed during 2014–2019 (Period 2; high-water years). In Wilcoxon signed rank pairwise comparisons, mean WQI scores increased significantly from 1.50 to 1.96 between Periods 1 and 2, respectively (p < 0.0001); by contrast, WMI scores remained numerically and statistically the same (3.38 vs 3.38, p = 0.42), while WFI scores dropped slightly, but not significantly (3.65 vs 3.59, p = 0.15). We hypothesize that WQI scores increased because of diluting effects from increased volume of water in wetlands due to higher water levels. Given the unpredictable influences of climate change on the pattern of Great Lakes water levels, index scores based on water-quality variables must be cautiously interpreted when they are used to compare sites across different water-level scenarios.
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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 | 0.002 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".