The effects of the lampricide 3-trifluoromethyl-4-nitrophenol (TFM) on fuel stores and ion balance in a non-target fish, the rainbow trout (Oncorhynchus mykiss)
Bibliographic record
Abstract
The pesticide 3-trifluoromethyl-4-nitrophenol (TFM) is used to control sea lamprey (Petromyzon marinus) populations in the Great Lakes through its application to nursery streams containing larval sea lampreys. TFM uncouples oxidative phosphorylation, impairing mitochondrial ATP production in sea lampreys and rainbow trout (Oncorhynchus mykiss). However, little else is known about its sub-lethal effects on non-target aquatic species. The present study tested the hypotheses that TFM exposure in hard water leads to (i) marked depletion of energy stores in metabolically active tissues (brain, muscle, kidney, liver) and (ii) disruption of active ion transport across the gill, adversely affecting electrolyte homeostasis in trout. Exposure of trout to 11.0mgl(-1) TFM (12-h LC50) led to increases in muscle TFM and TFM-glucuronide concentrations, peaking at 9h and 12h, respectively. Muscle and brain glycogen was reduced by 50%, while kidney and muscle lactate increased with TFM exposure. Kidney ATP and phosphocreatine decreased by 50% and 70%, respectively. TFM exposure caused no changes in whole body ion (Na(+), Cl(-), Ca(2+), K(+)) concentrations, gill Na(+)/K(+) ATPase activity, or unidirectional Na(+) movements across the gills. We conclude that TFM causes a mismatch between ATP supply and demand in trout, leading to increased reliance on glycolysis, but it does not have physiologically relevant effects on ion balance in hard water.
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) | 0.001 | 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".