Metrological Characterization of Spring Impact Hammer Calibration
Bibliographic record
Abstract
This article presents a proposed approach and mechanism for calibrating a spring hammer.The metrological characterization of the calibration results was discussed and the uncertainty of this measurement was evaluated.The proposed approach demonstrates a more applicable calibration method using a high accuracy force transducer to measure the spring force and a high-precision deflection measuring instrument to measure the corresponding deflection.Different relative errors are calculated to detect the applicability and accuracy of this approach.The overall relative expanded uncertainty is presented based on the introduced repeatability and reproducibility errors, side by side with the uncertainties of the reference force transducer and length measuring device used.The proposed calibration technique is used to calibrate both types of spring impact hammers; the multi-step hammer and the single-step hammer.Two examples for calibrating multistep hammer and another single-step hammer are presented with relative expanded uncertainties from 0.36% up to 5.37%.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".