Preferential intracellular pH regulation is a common trait amongst fishes exposed to high environmental CO2
Bibliographic record
Abstract
Acute (< 96 h) exposure to elevated environmental CO2 (hypercarbia) induces a pH disturbance in fishes that is often compensated by concurrent recovery of intra- and extracellular pH (pHi and pHe, respectively; coupled pH regulation). However, coupled pH regulation may be limited at PCO2 tensions far below levels that some fishes naturally encounter. Previously, four hypercarbia tolerant fishes had been shown to completely and rapidly regulate heart, brain, liver, and white muscle pHi during acute exposure to>4 kPa PCO2 (preferential pHi regulation) before pHe compensation was observed. Here we test the hypothesis that preferential pHi regulation is a wide spread strategy of acid-base regulation among fish by measuring pHi regulation in 10 different fish species that are broadly phylogenetically separated, spanning 6 orders, 8 families and 10 genera. Contrary to previous views, we show that preferential pHi regulation is the most common strategy for acid-base regulation within these fishes during exposure to severe acute hypercarbia and that this strategy is associated with increased hypercarbia tolerance. This suggests preferential pHi regulation may confer tolerance to the respiratory acidosis associated with hypercarbia and we propose that it is an exaptation that facilitated key evolutionary transitions in vertebrate evolution, such as the evolution of air breathing.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".