Page i BONE GROWTH DURING PUBERTY AND THE EFFECTS OF EXERCISE AND CALCIUM ON BONE MASS ACCRUAL A DOCTORAL THESIS
Bibliographic record
Abstract
Bone growth during puberty and the effects of exercise and calcium on bone mass Page a Dedication No thesis can be completed without the support and encouragement of all those around you. This thesis is dedicated to everybody that has played a part in its creation, from editing through to moral support. Acknowledgments The following are acknowledged in no particular order for their contribution to this thesis. Royal Childrens Hospital • Kay Gibbons for her nutritional expertise in the development of the calcium supplementation component of the intervention. • Robyn and Sue for cooking the foods used in the program. Murray Gouldbum Co-op • Dr. Wayne Sanderson and Michael Phillips for providing the milk minerals and the T-shirts for all the participants. University of Saskatchewan • Professor Don Bailey and Dr. Bob Mirwald for allowing me to work with them on their pediatric bone mineral accraal study and exposing me to the most significant learning experience of my life. • The staff of the College of Physical Education for making me feel so welcome during my time in Canada. • Susan Whiting for collaborating on the dietary calcium intake paper.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 0.006 |
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".