Merging economics, environmental science, and local knowledge to inform lake decision-making 
Bibliographic record
Abstract
Freshwater lakes are increasingly threatened by cultural eutrophication, caused by human activities such as agriculture and sewage outflows that over-fertilize waterbodies with nutrients like nitrogen and phosphorus, often triggering harmful algal blooms (HABs). Addressing these issues—which are both complex and costly—requires informed decision-making. Economic valuation of lake ecosystem services can contribute to informed decision-making by estimating the benefits of lake restoration and identifying acceptable trade-offs amongst ecosystem services—especially when designed using economics, environmental science, and local knowledge. We present a case study of collaboration with a community using an economic tool known as a discrete choice experiment survey to assess community preferences and willingness to pay for restoring a Canadian lake facing worsening water quality. Results show economic benefits of restoration far outweigh the costs, as well as strong preferences for non-use ecosystem services like biodiversity, highlighting the collective value placed on lake health in this community and contributing to targeted management efforts. These results contribute to the growing literature showing substantial benefits to society from restoring lakes, and showcase the value of drawing on multiple ways of knowing to guide environmental decision-making.
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.
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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".