Periphyton Response to Additions of Glucose and Hydrogen Peroxide as Control Measures of Harmful Algal Blooms
Bibliographic record
Abstract
A mesocosm-based study was conducted to assess the effect of glucose and hydrogen peroxide on periphyton communities. These chemicals have been found to be effective at controlling cyanobacteria blooms in the water column but their impact on attached communities is unknown. The experimental design included a total of 4 treatments: control (no chemicals; 3 replicates); hydrogen peroxide (3 replicates); glucose alone (3 different concentrations [no replicates]); and additive glucose (3 replicates). After 34 days, mean values of chlorophyll a were lower in all experimental treatments compared to the control; mean AFDM values were lower in all treatments except the unreplicated high glucose alone treatment. In contrast, mean autotrophic index values (AFDM/chlorophyll a) were greater in all treatments compared to the control, indicating heterotrophs were more resistant to the chemical treatments than autotrophs. Periphyton community biodiversity was much lower in the additive glucose and moderate glucose alone treatments than the hydrogen peroxide and control treatments. The relative abundance of the bacteria Asticcacaulis and Sphingorhabdus responded positively to the glucose treatments, whereas relative abundance of Nevskia and Caenimonas declined in both the hydrogen peroxide and glucose treatments. In terms of relative abundance, no cyanobacteria taxa were detected among the top 20 taxa. We conclude that the autotrophic component of periphyton communities is especially vulnerable to hydrogen peroxide and glucose treatments, and that any management strategy employing these chemicals should be aware of this potential impact.
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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) | 0.001 | 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 | 0.001 |
| 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".