Aquatic Condition Index: optimization of a rapid wetland assessment tool for evaluating urban wetland health
Bibliographic record
Abstract
Urbanization poses significant threats to wetland ecosystems, leading to habitat loss, hydrological alterations, and the introduction of invasive species that adversely affect essential ecosystem services. This widespread threat underscores the need to develop a robust management tool for gauging urban wetland health. The Aquatic Condition Index (ACI) was developed as a diagnostic tool for monitoring urban wetland health in Calgary, Alberta, Canada. The ACI evaluates wetland health by incorporating functional indicators (i.e., hydrological, ecological, and water quality functions) chosen by scientific experts to provide municipal wetlands with relative condition scores that can inform citywide habitat management budgeting and prioritization. Gathering the data necessary to generate wetland indicators for the ACI requires substantial financial resources, time, and a high degree of analytical expertise for data collection (e.g., field surveys). This investigation aimed to enhance the widespread applicability and cost-efficiency of wetland monitoring by optimizing the ACI. This optimization entailed a sensitivity-driven indicator reduction, which strategically minimizes the number of indicators essential for ACI calculations. Our findings demonstrate that the refined selection of indicators produces comparable results to the original ACI. This highlights the potential of transitioning to more rapid and cost-efficient monitoring methods, creating a streamlined approach to enhance the efficiency of monitoring and assessment processes. Ultimately, this approach can facilitate long-term urban wetland assessments and promote the sustainability and management of these vital urban features . Supplementary Information: The online version contains supplementary material available at 10.1007/s11252-024-01596-0.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".