Experimental validation of compliance models for LADD transmission kinematics
Bibliographic record
Abstract
Introduces new compliance models for LADD (linear to angular displacement device) transmissions which reduce, by an order of magnitude, inelastic model errors of up to 18% full scale over force and position operating ranges. Elastic models introduced so far were all based on fiber elasticity, which show an increase an LADD length from the inelastic length with force. The authors show that in experiments the opposite is true. The LADD is always shorter than predicted from the inelastic model. As the load force increases, the LADD length approaches the inelastic length. The authors found the cause for this fundamentally different elastic behavior to be fiber bending. The authors also employ one of the new models to improve the prediction of the kinematics of a CLADD, which consists of two concentric LADD devices. The new LADD models are essential for the design of LADD based systems, the online estimation of LADD forces, and accurate control.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".