Design and Manufacture of a Custom Ligament Loading Device for Use with Second Harmonic Generation Microscopy
Bibliographic record
Abstract
Ligament insertions into bone (entheses) represent a natural adaptation to severe material mismatch. Load is transferred from relatively flexible connective tissue to relatively inflexible bone over typically not more than a millimeter. Adequate load transfer at an insertion site is necessary for normal joint function while preventing injury. A few models have been used to assess different aspects of insertional mechanics, but all suffer from limitations. Most importantly, there has been an inability to observe the behaviour of entheses under applied load. An accurate description of enthesis load transfer mechanics has thus been lacking. A relatively new and powerful microscopic technology, second-harmonic generation (SHG), for which the University of Calgary has recently acquired an advanced microscope, has been shown to image movement on a microscale and is a promising tool to overcome the first of these difficulties, microscopic observation. The remaining difficulty remains the precise loading of ligaments during SHG imaging, highlighting the need for a custom-built loading device. Ligament loading is not an unfamiliar procedure and commercially available equipment exists to do so, however, the infrastructure for simultaneously loading and microscopically imaging entheses does not exist.The purpose of this work is to detail the device design process, from concept to manufacture, emphasizing the solutions to the design’s unique constraints and objectives and how they were determined. This includes the consolidation of a number of custom-machined components and commercially available hardware (for example: linear rail guides, strain gauges and precision motors).
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".