Interpolation of the Maresh diaphyseal length data for use in quantitative analyses of growth
Bibliographic record
Abstract
Abstract The Maresh data are commonly used in bioarcheological growth studies as a representation of diaphyseal growth in a modern and healthy group of children. However, several problems with the way the data were reported have limited its use in quantitative analyses of growth. In this paper, we present updated and interpolated values for long bone length for age for use in calculating z‐scores, percentages of expected length, and other quantitative measures of growth. The Maresh mean and mean + 1 standard deviation values for the sexes separately and combined were first corrected for radiographic magnification. Several modeling approaches were then evaluated. This testing suggested that the best fit was provided by two third‐order polynomials fit to data ≤24 and ≥24 months, respectively. The resulting regressions were used to calculate age‐specific mean and standard deviation values in 1‐month intervals from birth until 12 years (0–144 months). Differences between the original and new values are minimal and do not exceed 1 mm. However, as the old Maresh values required rounding age down to the last attained threshold by as much as 5 months, there are differences between z‐scores calculated with original versus new values of up to 2 z‐score units, especially in children under 3 years of age where growth velocity is highest. Although these updated values do not solve the problems that made age estimation from the Maresh data unadvisable, they will be of use to researchers in conducting more precise growth studies in bioarcheological contexts.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".