Determining in situ periphyton community responses to nutrient and atrazine gradients via pigment analysis
Bibliographic record
Abstract
Agrochemicals, including fertilizers and herbicides, are significant contributors of non-point source pollution to surface waters and have the potential to negatively affect periphyton. We characterized periphyton communities using pigment markers to assess the effects of nutrient enrichment and the herbicide atrazine with in situ experimental manipulations and by examining changes in community structure along existing agrochemical gradients. In 2008, the addition of nutrients (20 mg/L nitrate and 1.25 mg/L reactive phosphate), atrazine (20 μg/L) and a combination of both nutrients and atrazine had no significant effect on periphyton biomass or community structure in a stream periphytometer experiment. In 2009, similar experiments with higher concentrations of atrazine (200 μg/L) at two stream sites led to some minor effects. In contrast, at the watershed scale (2010) periphyton biomass (mg/m(2) chlorophyll a) increased significantly along correlated gradients of nitrate and atrazine but no direct effects of reactive phosphate were observed. Across the watershed, the average periphyton community was composed of Bacillariophyceae (60.9%), Chlorophyceae (28.1%), Cryptophyceae (6.9%) and Euglenophyceae (4.1%), with the Bacillariophyceae associated with high turbidity and the Chlorophyceae with nitrate enrichment. Overall, effects of nitrate on periphyton biomass and community structure superseded effects of reactive phosphate and atrazine.
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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 | 0.000 | 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".