Dissolved organic carbon from the upper Rio Negro protects zebrafish (Danio rerio) against ionoregulatory disturbances caused by low pH exposure
Bibliographic record
Abstract
The so-called "blackwaters" of the Amazonian Rio Negro are rich in highly coloured dissolved organic carbon (DOC), but ion-poor and very acidic, conditions that would cause fatal ionoregulatory failure in most fish. However these blackwaters support 8% of the world's ichthyofauna. We tested the hypothesis that native DOC provides protection against ionoregulatory dysfunction in this extreme environment. DOCs were isolated by reverse-osmosis from two Rio Negro sites. Physico-chemical characterization clearly indicated a terrigenous origin, with a high proportion of hydroxyl and phenolic sites, high chemical reactivity to protons, and unusual proteinaceous fluorescence. When tested using zebrafish (a model organism), Rio Negro DOC provided almost perfect protection against ionoregulatory disturbances associated with acute exposure to pH 4.0 in ion-poor water. DOC reduced diffusive losses of Na(+) and Cl(-), and promoted a remarkable stimulation of Na(+) uptake that otherwise would have been completely inhibited. Additionally, prior acclimation to DOC at neutral pH reduced rates of branchial Na(+) turnover, and provided similar protection against acid-induced ionoregulatory disturbances, even if the DOC was no longer present. These results reinforce the important roles that DOC molecules can play in the regulation of gill functions in freshwater fish, particularly in ion-poor, acidic blackwaters.
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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".