Bibliographic record
Abstract
Abstract Wetland biofilms were exposed to the herbicide glyphosate via in situ field exposures and controlled microcosm experiments to measure bioconcentration and metabolism of glyphosate by biofilm organisms. Glyphosate concentrations in biofilms were orders of magnitude higher than the surrounding water, bioconcentration factors averaged 835 and 199 in field- and lab-exposed biofilms, respectively. Glyphosate in water where it had been detected in biofilms at field-exposed sites ranged from below detection (<0.001 ppm) up to 0.13 ppm. Glyphosate bioconcentration in biofilms was inversely proportional to levels in the surrounding water, and the retention kinetics were similar to both adsorption and enzymatic models. Microorganisms present in both the water and biofilms metabolized glyphosate to its primary breakdown product aminomethyl phosphonic acid (AMPA), with increased rates of breakdown in and around the biofilms. Photosynthetic efficiency of the algae within the biofilms was not affected by 24 h glyphosate controlled exposures. Our results demonstrate the role of biofilms in improving wetland water quality by removing contaminants like glyphosate, but also as a potential exposure route to higher trophic levels via consumption. Due to bioconcentration of pesticides, exposure risk to organisms consuming or living in biofilms may be much higher than indicated by concentrations in ambient water samples.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".