Economic viability of proactive harmful algal bloom mitigation through attached algal growth
Bibliographic record
Abstract
Harmful algal blooms (HABs) affect fresh and saltwater bodies around the world, causing a variety of damages to the surrounding communities. The primary driver of HABs is nutrient pollution. One novel HAB mitigation solution is to employ large-scale attached algal growth systems that consume nutrients from the water and prevent downstream nutrient accumulation and large scale HAB events. The feasibility of technology deployment is dependent on the economic viability. A model was created to estimate HAB-related economic damages, with or without non-point source nutrient reduction solutions, using Lake Erie as a case study. In the model, HAB severity is predicted based on nutrient loads and is tied to economic indicators to estimate the HAB-related economic losses. Results show that on average, Lake Erie communities lose $142 M (± $29 M) year−1 from HABs without mitigation technology. Use of attached algal systems show an average net savings ranging between $29–42 M year−1 from HAB mitigation depending on the system configuration. Attached algal systems show greater positive cash flows when compared to farm-based best management practices, thus warranting additional testing and consideration as a potential HAB mitigation strategy. Additional considerations included stochastic uncertainty and increasing dissolved reactive phosphorus concentrations, which increased the effectiveness of attached algal systems.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".