Bibliographic record
Abstract
Introduction The purpose of this chapter is to assist the practitioner in the use and interpretation of multilevel models in the context of human growth research. The basic concepts of multilevel modelling are discussed and illustrated using a practical example. For detailed technical statistical discussions of multilevel modeling the reader is directed elsewhere (Goldstein, 1995; Kreft and de Leeuw, 1998; Snijders and Bosker 1999). As we know human physical growth is a highly regulated process. From conception to full maturity the change in size and shape is a continuous process. Many attempts have been made to find mathematical curves that can fit, and thus summarize, the process of human growth. There is considerable literature on the analysis of longitudinal growth data both for linear (Vandenberg and Falkner, 1965; Berkey and Reed, 1987) and non-linear (Jenss and Bayley, 1937; Preece and Baines, 1978) parametric models. Adjusting a mathematical model to a set of growth data is called growth curve fitting or growth modelling. Such growth models have had variable success in describing the pattern of human growth depending on the type of growth variable used, the precision of the measurement, the frequency and age range of the observations and the ability of the model to describe the growth curve (Karlberg, 1998). At the individual level what is required is a curve with relatively few variables, each capable of being interpreted in a biological meaningful way (Tanner, 1989).
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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 | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.075 | 0.014 |
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".