Dissipation of pesticides by stream biofilms is influenced by hydrological histories
Bibliographic record
Abstract
To evaluate the effects of hydrological variability on pesticide dissipation capacity by stream biofilms, we conducted a microcosm study. We exposed biofilms to short and frequent droughts (daily frequency), long and less frequent droughts (weekly frequency) and permanently immersed controls, prior to test their capacities to dissipate a cocktail of pesticides composed of tebuconazole, terbuthylazine, imidacloprid, glyphosate and its metabolite aminomethylphosphonic acid. A range of structural and functional descriptors of biofilms (algal and bacterial biomass, extracellular polymeric matrix (EPS) concentration, microbial respiration, phosphorus uptake and community-level physiological profiles) were measured to assess drought effects. In addition, various parameters were measured to characterise the dynamics of pesticide dissipation by biofilms in the different hydrological treatments (% dissipation, peak asymmetry, bioconcentration factor, among others). Results showed higher pesticide dissipation rates in biofilms exposed to short and frequent droughts, despite of their lower biomass and EPS concentration, compared to biofilms in immersed controls or exposed to long and less frequent droughts. High accumulation of hydrophobic pesticides (tebuconazole and terbuthylazine) was measured in biofilms despite the short exposure time (few minutes) in our open-flow microcosm approach. This research demonstrated the stream biofilms capacity to adsorb hydrophobic pesticides even in stressed drought environments.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".