Adaptive Capacity for eutrophication governance of the Laurentian Great Lakes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Great Lakes are the largest freshwater body in the world, holding 20% of the worlds freshwater. Together, Lakes Superior, Michigan, Huron, Erie and Ontario, are home to over 35million Americans and Canadians, a factor that lead to severe human related stress to the lakes’ ecosystem. The eutrophication of Lake Erie is one manifestation of this anthropogenic stress from nutrient enrichment from farming, sewage treatment plant discharges, airborne emissions and nutrient flows from paved surfaces. This paper examines the eutrophication of Lake Erie and shows that it is a wicked problem that can benefit from an adaptive governance approach. More specifically, it proposes a framework for assessing adaptive capacity and tests this framework through key informant interviews in the case where adaptive capacity was displayed; a Lake Erie that went from severe eutrophication the 1960s to significant nutrient reduction and restoration of the Lake Erie ecosystem in the 1990s. This research also aims to identify gaps in adaptive capacity for current eutrophication governance of Lake Erie.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.000 | 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 it