Bias and precision of algorithms in estimating the cross-sectional area of rat tail tendons
Bibliographic record
Abstract
The cross-section area (CSA) of rat tail tendons (RTTs), a common experimental model in biomechanics and mechanobiology, is often approximated using circle or ellipse models. This assumption may nevertheless be faulty given the sensitivity of the mechanical properties on CSA estimation. To investigate this issue, we designed a new optic micrometer to be used under a stereomicroscope. Images of specimen projections were captured at angular increments and specimen edges were localized within a local reference frame using an image analysis algorithm based on contrast. The cross-sectional areas estimated using four algorithms (single measurement circle, multiple measurement circle, two degrees of freedom ellipse, three degrees of freedom ellipse) were compared to those obtained using the best algorithm currently described in the literature: the profile reconstruction algorithm. We showed that the four tested algorithms exhibit moderate but uniform bias (mean systematic error between 7 and 11%) with very non-uniform precision, varying from excellent to very poor (adjusted root mean square deviation between 0 and 19%). The maximum CSA error was found to be as high as 99%. We therefore recommend avoiding the algorithms approximating the RTT CSA using circle or ellipse models in studies where accurate estimation of the CSA is required.
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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 | 0.018 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".