The acute osmoregulatory effects of low <scp>pH</scp> and Cu, alone and in combination, on the dwarf cichlid ( <scp> <i>Apistogramma agassizii</i> </scp> ) in Rio Negro blackwater and Rio Solimões whitewater: Flux rates of ions, nitrogenous wastes and water
Bibliographic record
Abstract
Abstract Increases in anthropogenic activities in the Amazon have led to pollution from trace metals, including copper. Dissolved organic carbon (DOC) is known to protect against metal toxicity and ionoregulatory disturbances in Amazonian fish, particularly at low pH. However, little is known about the effects of DOC and trace metals, such as copper, on the branchial water transport pathways. Water moves across the gills of fish through two distinct pathways: paracellularly through tight junctions and transcellularly by diffusion through aquaporins. In the present study, we evaluated the effects of copper (nominally 200 μg L −1 ) on diffusive water flux rate (transcellular water movement), paracellular permeability ([ 3 H]‐polyethylene glycol‐4000 clearance), ion balance (net sodium, potassium and chloride fluxes) and nitrogenous waste (ammonia and urea) excretion in the dwarf cichlid, Apistogramma agassizii. Exposures were conducted in control water (low ions, very low DOC), in filtered Rio Negro (RN) blackwater (low ions, high DOC) and in filtered Rio Solimões (RS) whitewater (higher ions, intermediate DOC) at pH 7 and pH 4. Copper increased ion losses in control water, especially at low pH; RN water protected against these effects, whereas RS water did not, reflecting greater complexation of free Cu 2+ ions by RN DOC. Our results are the first to show that copper tends to inhibit urea‐N excretion as well as ammonia excretion, and also decreases branchial water transport both transcellularly and through tight junctions. The protective effects of DOC against the disturbances caused by copper were dependent on the source of the DOC and the water pH.
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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".