The influence of environmental calcium concentrations on calcium flux, compensatory drinking and epithelial calcium channel expression in a freshwater cartilaginous fish
Bibliographic record
Abstract
Calcium metabolism and mRNA levels of the epithelial calcium channel (ECaC) were examined in a freshwater cartilaginous fish, the lake sturgeon Acipenser fulvescens. Lake sturgeon were acclimated for ≥2 weeks to 0.1 (low), 0.4 (normal) or 3.3 (high) mmol l(-1) environmental calcium. Whole-body calcium flux was examined using (45)Ca as a radioactive marker. Net calcium flux was inward in all treatment groups; however, calcium influx was greatest in the low calcium environment and lowest in the high calcium environment, whereas efflux had the opposite relationship. A significant difference in the concentration of (45)Ca in the gastrointestinal tract (GIT) of fish in the low calcium environment led to the examination of drinking rate and calcium flux across the anterior-middle (mid) intestine. Drinking rate was not different between treatments; however, calcium influx across the mid-intestine in the low calcium treatment was significantly greater than that in both the normal and high calcium treatments. The lake sturgeon ECaC was 2831 bp in length, with a predicted protein sequence of 683 amino acids that shared a 66% identity with the closest sequenced ECaCs from the vertebrate phyla. ECaC mRNA levels were examined in the gills, kidney, pyloric caeca, mid-intestine and spiral intestine. Expression levels were highest in the gills, then the kidneys, and were orders of magnitude lower in the GIT. Contrary to existing models for calcium uptake in the teleost gill, ECaC expression was greatest in high calcium conditions and kidney ECaC expression was lowest in low calcium conditions, suggesting that cellular transport mechanisms for calcium may be distinctly different in these freshwater cartilaginous fishes.
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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".