The relationship between nephron number, kidney size and body weight in two inbred mouse strains
Bibliographic record
Abstract
While some reports in humans have shown that nephron number is positively correlated with height, body weight or kidney weight, other studies have not reproduced these findings. To understand the impact of genetic and environmental variation on these relationships, we examined whether nephron number correlates with body weight, kidney planar surface area, or kidney weight in two inbred mouse strains with contrasting kidney sizes but no overt renal pathology: C3H/HeJ and C57BL/6J. C3H/HeJ mice had smaller kidneys at birth and larger kidneys by adulthood, however there was no significant difference in nephron number between the two strains. We did observe a correlation between kidney size and body weight at birth and at adulthood for both strains. However, there was no relationship between nephron number and body weight or between nephron number and kidney size. From other studies, it appears that a greater than two-fold variation is required in each of these parameters in order to demonstrate these relationships, suggesting they are highly dependent on scale. Our results are therefore not surprising since there was a less than two-fold variation in each of the parameters examined. In summary, the relationship between nephron number and body or kidney size is most likely to be demonstrated when there is greater phenotypic variation either from genetic and/or environmental factors.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".