Bibliographic record
Abstract
Handedness is challenging to estimate on skeletonized human remains when muscular markers of it are not available. This preliminary study anthropometrically explores if human teeth could be used to aid in identification of handedness. The dental data for this research is collected from a total of fifty (N=50) living adult volunteers with good dental health of which thirty‐nine (N=39) were right handed and eleven (N=11) left‐handed. Non‐invasive anthropometric data collected via digital dental caliper consists of the Maxillary and the Mandibular quadrants in which permanent incisors and canines were analyzed. The study reconfirms that males on average have larger teeth than females. The lateral incisors were not taken into consideration as they show minimal variations across the quadrants. The left‐handed males measure slightly larger central Maxillary incisors and canines at the left and the right quadrant than the right‐handed males. The left‐handed females have smaller central incisors than the right‐handed females on the Maxillary quadrants, but slightly larger central incisors on the Mandibular quadrants. Mandibular right quadrant central incisors are slightly larger among the left‐handed than they are among the right‐handed individuals of both sex. Support or Funding Information SWC This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".