50 years of scanning electron microscopy of bone—a comprehensive overview of the important discoveries made and insights gained into bone material properties in health, disease, and taphonomy
Bibliographic record
Abstract
Bone is an architecturally complex system that constantly undergoes structural and functional optimisation through renewal and repair. The scanning electron microscope (SEM) is among the most frequently used instruments for examining bone. It offers the key advantage of very high spatial resolution coupled with a large depth of field and wide field of view. Interactions between incident electrons and atoms on the sample surface generate backscattered electrons, secondary electrons, and various other signals including X-rays that relay compositional and topographical information. Through selective removal or preservation of specific tissue components (organic, inorganic, cellular, vascular), their individual contribution(s) to the overall functional competence can be elucidated. With few restrictions on sample geometry and a variety of applicable sample-processing routes, a given sample may be conveniently adapted for multiple analytical methods. While a conventional SEM operates at high vacuum conditions that demand clean, dry, and electrically conductive samples, non-conductive materials (e.g., bone) can be imaged without significant modification from the natural state using an environmental scanning electron microscope. This review highlights important insights gained into bone microstructure and pathophysiology, bone response to implanted biomaterials, elemental analysis, SEM in paleoarchaeology, 3D imaging using focused ion beam techniques, correlative microscopy and in situ experiments. The capacity to image seamlessly across multiple length scales within the meso-micro-nano-continuum, the SEM lends itself to many unique and diverse applications, which attest to the versatility and user-friendly nature of this instrument for studying bone. Significant technological developments are anticipated for analysing bone using the SEM.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".