The effect of environmental factors on transepithelial potential in a model Amazonian teleost, the tambaqui ( <scp> <i>Colossoma macropomum</i> </scp> ): Implications for sodium balance in harsh environments
Bibliographic record
Abstract
Abstract The tambaqui ( Colossoma macropomum , G. Cuvier 1818) thrives both in the ion‐poor waters of the Amazon and in commercial aquaculture. In both, environmental conditions can be harsh due to low ion levels, occasional high salt challenges (in aquaculture), low pH, extreme PO 2 levels (hypoxia and hyperoxia), high PCO 2 levels (hypercapnia), high ammonia levels (in aquaculture), and high and low temperatures. Ion transport across the gill is affected by active transport processes, passive diffusive permeability, ion concentrations (the chemical gradient), and transepithelial potential (TEP, the electrical gradient). The latter is a very important indicator of ionoregulatory status but is rarely measured. Using normoxic, normocapnic, ion‐poor, low–dissolved organic carbon (DOC) well water (27°C, pH 7.0) as the acclimation and reference condition, we first confirmed that the strongly negative TEP (−22.3 mV inside relative to the external water) is a simple diffusion potential. We then evaluated the effects on TEP of more complex waters from the Rio Negro (strong hyperpolarization) and Rio Solimões (no significant change). Additionally, we have quantified significant effects of acute, realistic changes in environmental conditions—low pH (depolarization), hypercapnia (depolarization), hypoxia (depolarization), hyperoxia (hyperpolarization), elevated NaCl concentrations (depolarization), and elevated NH 4 Cl concentrations (depolarization). The TEP responses help explain many of the changes in net Na + flux rates reported in the literature. We have also shown marked effects of temperature on TEP and unidirectional Na + flux rates (hyperpolarization and decreased fluxes at 21°C, depolarization and increased fluxes at 33°C) with no changes in net Na + flux rates. Calculations based on the Nernst equation demonstrate the importance of the TEP changes in maintaining net Na + balance.
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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".