Biomechanical Measurement Error Can Be Caused by Fujifilm Thickness: A Theoretical, Experimental, and Computational Analysis
Bibliographic record
Abstract
This is the first study to quantify the measurement error due to the physical thickness of Fujifilm for several material combinations relevant to orthopaedics. Theoretical and experimental analyses were conducted for cylinder-on-flat indentation over a series of forces (750 and 3000 N), cylinder diameters (0 to 80 mm), and material combinations (metal-on-metal, MOM; metal-on-polymer, MOP; metal-on-bone, MOB). For the scenario without Fujifilm, classic Hertzian theory predicted the true line-type contact width as WO={(8FDcyl)/(πLcyl)[(1-νcyl2)/Ecyl+(1-νflat2)/Eflat]}1/2 , where F is compressive force, Dcyl is cylinder diameter, Lcyl is cylinder length, νcyl and νflat are cylinder and flat Poisson’s ratios, and Ecyl and Eflat are cylinder and flat elastic moduli. For the scenario with Fujifilm, experimental measurements resulted in contact widths of WF=0.1778×F0.2273×D0.2936 for MOM tests, WF=0.0449×F0.4664×D0.4201 for MOP tests, and WF=0.1647×F0.2397×D0.3394 for MOB tests, where F is compressive force and D is cylinder diameter. Fujifilm thickness error ratio WF/WO showed a nonlinear decrease versus cylinder diameter, whilst error graphs shifted down as force increased. Computational finite element analysis for several test cases agreed with theoretical and experimental data, respectively, to within 3.3% and 1.4%. Despite its wide use, Fujifilm’s measurement errors must be kept in mind when employed in orthopaedic biomechanics research.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".