Bibliographic record
Abstract
Biomechanics is a branch of the field of bioengineering, which we define as the application of engineering principles to biological systems. Most bioengineering is applied to humans, and in this book the primary emphasis will be on Homo sapiens . The bioengineer seeks to understand basic physiological processes, to improve human health via applied problem solving, or both. This is a difficult task, since the workings of the body are formidably complex. Despite this difficulty, the bioengineer's contribution can be substantial, and the rewards for success far outweigh the difficulties of the task. Biomechanics is the study of how physical forces interact with living systems. If you are not familiar with biomechanics, this might strike you as a somewhat esoteric topic, and you may even ask yourself the question: Why does biomechanics matter? It turns out that biomechanics is far from esoteric and plays an important role in diverse areas of growth, development, tissue remodeling and homeostasis. Further, biomechanics plays a central role in the pathogenesis of some diseases, and in the treatment of these diseases. Let us give a few specific examples: How do your bones “know” how big and strong to be so that they can support your weight and deal with the loads imposed on them? Evidence shows that the growth of bone is driven by mechanical stimuli [1]. More specifically, mechanical stresses and strains induce bone cells ( osteoblasts and osteoclasts ) to add or remove bone just where it is needed. Because of the obvious mechanical function played by bone, it makes good sense to use mechanical stress as the feedback signal for bone growth and remodeling. But biomechanics also plays a “hidden” regulatory role in other growth processes, as the next example will show. […]
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.407 | 0.231 |
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".