Effects of water pH on the uptake and elimination of the piscicide, 3-trifluoromethyl-4-nitrophenol (TFM), by larval sea lamprey
Bibliographic record
Abstract
Invasive sea lamprey (Petromyzon marinus) populations in the Great Lakes are controlled by applying the piscicide, 3-trifluoromethyl-4-nitrophenol (TFM), to infested streams with larval sea lamprey (ammocoetes). While treatment mortality is > 90%, surviving lamprey, called residuals, can undermine control efforts. A key determinant of TFM effectiveness is water pH, which can fluctuate daily and seasonally in surface waters. The objectives of this research were to evaluate the influence of pH on the uptake, elimination, and accumulation of TFM by larval sea lamprey using radio-labeled TFM (14C-TFM), when exposed to a nominal concentration of 4.6 mg TFM L− 1 or 7.6 mg TFM L− 1, 3 h or 1 h, respectively. TFM uptake rates were approximately 5.5-fold greater at low pH (6.86) compared to the high pH (8.78), most likely due to the unionized, lipophilic form of TFM existing in greater amounts at a lower pH. In contrast, elimination rates following the injection of 85 nmol TFM g− 1 body mass were 1.7–1.8 fold greater at pH 8.96 than at pH 6.43 during 2–4 h of depuration in TFM-free water. Greater initial excretion rates at pH 8.96 were presumably due to predicted increases in outward concentration gradients of un-ionized TFM. The present findings suggest that TFM is mainly taken-up in its un-ionized form, more lipophilic form, but there is also significant uptake of the ionized form of TFM via an unknown mechanism. Moreover, we provide an explanation to how small increases in pH can undermine lampricide treatment success increasing residual lamprey populations.
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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".