Characteristics of the Stress-Generated Electrical Potentials in Bone Induced by Mechanical Loading
Bibliographic record
Abstract
Since the discovery of stress-generated potentials (SGPs) in bone by Fukada and Yasuda in 1957, researchers have tried to understand their origin and function in the maintenance of bone. There have been a variety of methods attempting to quantify these SGPs in both wet and dry bone. In this study, I prepared both dry and wet beams of cortical bovine bone and subjected them to mechanical deformation in cantilever bending. Mechanical testing was performed to explore how the magnitude of the SGPs was affected by hydration levels, strain, and pressure gradients associated with various load magnitudes and deformation rates. Signals that were collected from the dry bone samples were attributed to motion artifact resulting from the movement of the materials testing machine and load cell. The SGPs from wet bone, on the other hand, consistently produced exponentially decaying signals following deformation that were maintained throughout held deformation and produced an SGP of opposite magnitude upon release of deformation. \nThe exponentially decaying SGP signal produced after application of a step load to wet bone samples was determined to fit a two-term exponential equation (V(t) = Aet/τ1 + Cet/τ2). The first term, made up of the A-coefficient and τ1, was found to be dependent on deformation rate whereas the second term, containing the C-coefficient and τ2, was dependent on load magnitude. The sum of the two coefficients determine the maximum voltage the SGP can reach. \nAdditionally, samples were left to air dry for one hour and tested intermittently throughout that time period. SGP signals diminished significantly over the hour, therefore, it has been concluded that the majority of the SGP signal is due to streaming potentials caused by ionic fluid movement within the bone upon deformation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.000 |
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 teacher head, 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".