Historical political ecology as qualitative social-ecological system analysis in the Maumee River Watershed
Bibliographic record
Abstract
Threats to water security are on the rise globally. In the Great Lakes, major threats arise from watershed land-use practices promoting chemical and nutrient pollution. In the Maumee River Watershed (MRW), which discharges into the western basin of Lake Erie, nutrient inputs associated with harmful algal bloom events (HABs) have increased in frequency and severity over the last several decades. This includes forcing the historic shut down of the City of Toledo’s drinking water supply to 400,000 residents in 2014. Conditions which favor HABs did not appear overnight. We trace the history of land-use practices that transformed the structure and function of the MRW from a balanced system into a source of nutrient dynamics favoring HABs. Successful policy intervention must treat the MRW as a complex adaptive system operating at multiple scales at the intersection of agricultural practices and conflicting socioeconomic drivers, confounding traditional interventions that focus on single factors such as water quality or fertilizer use regulation. We conclude with three interventions that policymakers can employ to help tip the scales back into an ecologically balanced system.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".