The Economic Costs of Algal Blooms: Great Lakes Evidence and Research Priorities
Bibliographic record
Abstract
Over the past two decades there has been a re-emergence of harmful and nuisance algal blooms in Lake Erie and, to a lesser extent, Lake Ontario due mainly to increasing phosphorus loadings from non-point agricultural sources. Citizens on both sides of the Canada-U.S. border face economic costs due to these blooms. This presentation will draw upon two studies undertaken to evaluate these costs for the province of Ontario using standard economic approaches. These studies suggest that algal blooms impose considerable costs today (hundreds of millions of dollars annually) and that these costs will grow if blooms are left unchecked. The studies also consider the amount by which costs might fall if policy measures were taken to control phosphorus loadings, providing an economic basis for assessing the desirability of control. The presentation will also consider the broader questions around this kind of analysis, which is increasingly looked to by decision makers as an aid to policy development. These broader issues include the state of the socio-economic data available to undertake cost evaluations, the suitability of economic analysis as an aid to decision-making around freshwater quality and the main barriers to greater application of the approach.
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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 | 0.010 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".