Socioeconomic and Environmental Proxies for Comparing Freshwater Ecosystem Service Threats across International Sites: A Diagnostic Approach
Bibliographic record
Abstract
In this work, we develop and test proxy-based diagnostic tools for comparing freshwater ecosystem services (FWES) risks across an international array of freshwater ecosystems. FWES threats are increasing rapidly under pressure from population, climate change, pollution, land use change, and other factors. We identified spatially explicit FWES threats estimates (referred to as threat benchmarks) and extracted watershed-specific values for an array of aquatic ecosystems in the Western Hemisphere (Ramsar sites). We compared these benchmark values to values extracted for sites associated with an international FWES threat investigation. The resulting benchmark threats appeared to provide a meaningful context for the diagnostic assessment of study site selection by revealing gaps in coverage of the underlying socio-environmental problem. In an effort to simplify the method, we tested regularly updated environmental and socioeconomic metrics as potential proxies for the benchmark threats using regression analysis. Three category proxies, aggregated from (i) external (global to regional, climate-related), (ii) internal (watershed management-related), and (iii) socioeconomic and governance related proxies produced strong relationships with water supply threat benchmarks, but only weak relationships with biodiversity-related and nutrient regulation benchmark threats. Our results demonstrate the utility of advancing global FWES status and threat benchmarks for organizing coordinated research efforts and prioritizing decisions with regard to international socio-environmental problems.
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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 | 0.011 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".