Evidence for behavioral preference toward environmental concentrations of urban-use herbicides in a model adult fish
Bibliographic record
Abstract
Fish live in waters of contaminant flux. In three urban, fish-bearing waterways of British Columbia, Canada, we found the active ingredients of WeedEx, KillEx, and Roundup herbicide formulations (2,4-D, dicamba, glyphosate, and mecoprop) at low to high ng/L concentrations (0.26 to 309 ng/L) in routine conditions, i.e., no rain for at least one week. Following rain, these concentrations increased by an average of eightfold, suggesting runoff as a major route of herbicide introduction in these waterways. To determine whether fish might be able to limit point-source exposures through sensory-driven behaviors, we introduced pulses of representative herbicide mixtures to individual adult zebrafish (a model species) in flow-through tanks. Fish did the opposite of limit exposure; they chose to spend more time in pulses of herbicide mixtures representative of those that may occur with rain events. This attraction response was not altered by a previous 4-d exposure to lower concentrations of the mixtures, suggesting fish will not learn from previous exposures. However, previous exposures did alter an attraction response to an amino acid prevalent in food (L-alanine). The present study demonstrates that fish living within urban waterways may elect to place themselves in herbicide-contaminated environments and that these exposures may alter their behavioral responses to cues necessary for survival.
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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) | 0.001 | 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".