Bibliographic record
Abstract
This study estimates the economic value of lake water quality changes in South Eastern region of Ontario using the hedonic price method. The research combines 58,085 house transaction data between 2005-2014 time periods and water quality (WQ) data from 494 unique lakes. I examine the effect of total phosphorus and Secchi depth (SD) on house price using a log-linear regression. Considering SD as the variable of interest, results indicate that house buyers are willing to pay a 1.9% higher price for a one-meter improvement in SD if the house is located close to the lake WQ station. The marginal willingness to pay (MWTP) for SD reaches the peak, $7,627 per meter, for houses located within 500 meters to 750 meters distance to the lake stations. However, the price premium starts to decrease as the lake distance increases; house buyers are willing to pay 4.4% less for a marginal increase in SD if the house is located within 2,000 to 3,000 meters of the WQ stations. I assess the robustness of the results across the alternative data specification and estimate the highest level of MWTP for water quality ($6, 142) considering houses within 3 kilometres to the lake stations. The estimated local benefits can inform the design of WQ improvement programs.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.019 | 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 teacher head, 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".