Dual phosphorus and nitrogen nutrient reduction will be more effective than a phosphorus‐only reduction in mitigating diatom and cyanobacterial blooms in Lake Erie, <scp>USA</scp>–Canada
Bibliographic record
Abstract
Abstract Lake Erie, USA–Canada, plays an important ecological and socioeconomic role but has suffered from chronic eutrophication. In particular, western Lake Erie (WLE) is the site of harmful algal blooms (HABs) which are suspected of being driven by excessive nutrient (phosphorus (P) and nitrogen (N)) inputs. During 2022 and 2023, in situ nutrient dilution and addition bioassays were conducted at a WLE bloom‐impacted location to investigate whether a nutrient reduction regime would be effective in limiting phytoplankton growth during the June diatom‐dominated spring blooms and August cyanobacteria‐dominated summer blooms. The primary objectives of this experiment were to (1) Determine if a proposed 40% P‐alone reduction would effectively reduce phytoplankton growth and mitigate blooms and (2) assess whether reductions in both P and N are more effective in controlling phytoplankton biomass than exclusive reductions in either N or P. Samples were analyzed for nutrient concentrations and growth rate responses for specific algal groups, utilizing diagnostic (for major algal groups) photopigments. Results indicated that although both 20% and 40% dilutions led to lower phytoplankton biomass and growth rates, 40% reductions were more effective. Our results support the USA–Canada Great Lakes Water Quality Agreement recommendation of a 40% P reduction, but also indicate that a parallel reduction of N input by 40% would be most effective in controlling bloom magnitudes. Overall, our findings underscore the recommendation that a year‐round dual N and P 40% reduction is needed for long‐term control of eutrophication and algal blooms, including cyanobacteria and diatoms, in Lake Erie.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".