Bibliographic record
Abstract
The skeletal system is made up of a number of different tissues that are specialized forms of connective tissue . The primary skeletal connective tissues are bone, cartilage, ligaments, and tendons. The role of these tissues is mainly mechanical, and therefore they have been well studied by biomedical engineers. Bones are probably the most familiar component of the skeletal system, and we will begin this chapter by considering their function, structure, and biomechanical behavior. Introduction to bone The human skeleton is a remarkable structure made up of 206 bones in the average adult (Fig. 9.1). The rigid bones connect at several joints, a design that allows the skeleton to provide structural support to our bodies, while maintaining the flexibility required for movement (Table 9.1). Bones serve many functions, both structural and metabolic, including: providing structural support for all other components of the body (remember that the body is essentially a “bag of gel” that is supported by the skeleton) providing a framework for motion, since muscles need to pull on something rigid to create motion offering protection from impacts, falls, and other trauma acting as a Ca 2+ repository (recall that Ca 2+ is used for a variety of purposes, e.g., as a messenger molecule in cells throughout the body) housing the marrow, the tissue that produces blood cells and stem cells. In the following sections, we focus our discussion on the biomechanical functions of bone, and to do so we start by describing the composition and structure of bone tissue.
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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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.057 | 0.030 |
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".