Anticipated human population and climate change effects on algal blooms of a toxic haptophyte in the south-central USA<sup>1</sup>This article is derived from a special session entitled “A New Hydrology: Inflow Effects on Ecosystem Form and Functioning” that took place at the February 2011 ASLO Aquatic Sciences conference in San Juan, Puerto Rico.
Bibliographic record
Abstract
Effects of inflow on phytoplankton dynamics and assemblage structure have long been an interest of ecologists and resource managers, especially when they are linked to the incidence of harmful algal blooms. The frequency and magnitude of Prymnesium parvum bloom-preventing inflows likely in a drier landscape of south-central USA was explored, along with the relative importance of various factors important to blooms. We show that the number of large inflow events necessary to prevent blooms might decrease between 25% and 65% under drier conditions likely for this region. Long duration inflow events that are critical to lake flushing could nearly disappear, with inflow events lasting longer than 20 days decreasing 40-fold. These findings suggest that the frequency of P. parvum blooms and fish-kill events might increase in this region with human population and climate change. Multivariate analyses of monitoring data from multiple lakes indicate that other factors may be equally important to bloom occurrences. Inverse trends between toxic bloom events and nutrient concentrations, cyanobacteria, and lower pH are apparent. During periods when P. parvum populations were not toxic, an inverse relationship with zooplankton was observed. These other factors might be harnessed to mitigate P. parvum blooms in the future when inflows are reduced.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".