Bibliographic record
Abstract
Re: Promoting optimal monitoring of child growth in Canada: Using the new World Health Organization growth charts – Executive Summary. Paediatr Child Health 2010;15(2):77–83. To the Editor: Anyone with a basic education understands that for any object or creature that is in proportion, volume and mass increase with the cube of the length. Presumably, the concept of body mass index (BMI) was invented by someone (perhaps a physician) with a more limited basic knowledge than this, before it was made popular by Ancel Keys in 1972. Because BMI uses the square of the height rather than the cube, anyone who is tall but normally proportioned will tend to have a high BMI and anyone who is short (including children) will tend to have a low BMI, even if they are relatively obese. The variability of BMI only becomes worse when plotted against age, as height becomes another variable. So why does the Canadian Paediatric Society support the use of such a ridiculous measurement? BMI should not even be used in adults! Weight for height curves, or the Ponderal Index (mass divided by the cube of the length), are far more appropriate indicators for leanness or obesity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.038 | 0.024 |
| Insufficient payload (model declined to judge) | 0.015 | 0.010 |
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".