Staying afloat: The effect of algae contamination on <scp>Lake Erie</scp> housing prices
Bibliographic record
Abstract
Abstract Lake Erie has experienced unprecedented harmful algal blooms since the early 2000s, prompting the 2012 Great Lakes Water Quality Agreement between the United States and Canada, which aims to reduce lake‐wide phosphorous loadings by 40%. Little is known about the economic benefits from this agreement, especially to near lake homeowners. We provide key information on the benefits of harmful algal bloom cleanup by linking housing transactions in 2003 to 2015 from seven Ohio counties bordering Lake Erie with measures of water quality using remote‐sensing images. We further control for endogenous algae production using instrumental variables derived from hydrological processes that link Maumee River runoff to nutrient concentrations in Lake Erie. Using a semiparametric approach, we find the impact of harmful algal blooms on housing prices is spatially limited to properties within 1.2 km of Lake Erie. For the average near lake homeowner, a 1 μg/L increase in algae concentrations is expected to decrease property values by 1.7% ($2205). In aggregate, fulfilling the Great Lakes Water Quality Agreement will provide a yearly benefit of up to $42.9 million, fully covering the current annual expenditure on water quality improvement.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".