Effect of load fixture design on sensitivity of an extended octagonal ring (EOR) transducer.
Bibliographic record
Abstract
Identical loading and support fixtures were fabricated to apply vertical compressive loads at two points with varying spacing on the faces of an Extended Octagonal Ring (EOR) transducer. A calibration apparatus employing an air cylinder fitted with a strain gage load cell was assembled to apply and measure vertical load on the EOR. Calibrations were performed to determine the effect of spacing between the two loading points on EOR sensitivity. At moderate load point spacings, a small decrease in EOR sensitivity was noted with increasing load point spacing. The EOR sensitivity rapidly decreased as the load points approached the ring sections, with approximately 40% reduction in sensitivity when the load points were near the sloped outer surface of the ring sections. Effect of non-flat loading and support fixtures was evaluated by calibrating the EOR with different torques applied to the mounting bolts and with varying load point spacings. Tension in the mounting bolts created an initial bending moment in the EOR at zero applied load. Bolt torque had little effect on EOR sensitivity at small load point spacings, but high bolt torque decreased the EOR sensitivity at large load point spacings. When the loading points were over the ring sections, the changing the bolt torque from zero to maximum changed the EOR offset (zero load signal) by an amount approximately equal to the EOR design capacity. These results demonstrate the importance of careful attention design of the load and support fixtures and calibration procedures for an EOR to achieve optimum performance.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".