Environmental Studies of Cyanobacterial Harmful Algal Blooms Should Include Interactions with the Dynamic Microbiome
Bibliographic record
Abstract
Biology is complicated. Nowhere might this be more true than in aquatic systems. Lakes, especially those in temperate regions, commonly undergo seasonal dynamics in the background of constant anthropogenic insult. Among the ecosystem level responses are cyanobacterial blooms (cHABs), which render water bodies unusable and potentially toxic. High-profile interruptions of access to potable water affecting >400 000 residents of Toledo, OH in 2014 and more than >2 000 000 residents of Wuxi, China in 2007 highlight this problem. (1) Indeed, global-scale observations report an increase in the size and frequency of cHABs on six of the seven continents. (2) While eutrophication is clearly a primary driving force, climate change, and invasive species are also factors. Ultimately, research into the specific drivers of cHABs continues to provide unclear, and often contradictory mechanisms of bloom formation: an example of this is the ongoing debate on the roles of nitrogen and phosphorus as bloom promoters. (3) Meanwhile, cyanobacteria continue to dominate large freshwater systems despite decades of nutrient control, albeit these controls have been largely phosphorus-focused. There is also tremendous focus on both the physiology and ecology of key cyanobacteria genera (e.g., Microcystis and Planktothrix) which produce the toxic secondary metabolite microcystin, a compound originally known as “Fast Death Factor”. (4) However, despite all efforts and tremendous progress, the picture remains complicated, with contradictions, for example, on the roles of pH, temperature, and viruses in constraining or promoting cyanobacterial harmful algal blooms or their production of toxin(s). (3) In addition, an important potential cause of variability in both lab and field experiments is frequently overlooked: the co-occurring microbes which numerically represent a majority of the microbial community.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".