Data from: Artificial selection sheds light on developmental mechanisms of limb elongation
Bibliographic record
Abstract
Species diversity in limb lengths and proportions is thought to have evolved adaptively in the context of locomotor and habitat specialization, but the heritable cellular processes that drove this evolution within species are poorly understood. In this study, we take a novel “micro-evo-devo” approach, using artificial selection on relative limb length to amplify phenotypic variation in a population of mice, known as Longshanks, to examine the cellular mechanisms of postnatal limb development that contribute to intraspecific limb length variation. Cross-sectional growth data indicate that differences in bone length between Longshanks and random-bred controls are not due to prolonged growth, but to accelerated growth rates. Histomorphometric and cell proliferation assays on proximal tibial growth plates show that Longshanks’ increased limb bone length is associated with an increased number of proliferative chondrocytes. In contrast, we find no differences in other growth plate cellular features known to underlie interspecific differences in limb bone size and shape, such as the rates of chondrocyte proliferation or the size and number of hypertrophic cells in the growth plate. These data suggest that small differences among individuals in the number of proliferating chondrocytes are a potentially important determinant of selectable intraspecific variation in individual limb bone lengths, independent of body size.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.072 | 0.057 |
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".