Jurisprudence and Expert Witness Testimony
Bibliographic record
Abstract
In today’s society, the purpose and value of forensic dental age estimation has expanded to meet a variety of medicolegal needs such as providing an estimated age at death. With over 40,000 unidentied bodies and 100,000 missing individuals at any given time in the United States alone, age estimation signicantly narrows the search possibilities for law enforcement. In mass disaster and cluster victim situations, age segregation aids in the process of identication. en there is forensic application to aid authorities in determining eligibility for social benets, the age of license and age of legal majority. e age of legal license should not be confused with the legal age of majority. In legal terms, “license” means “permission” and is the age at which the law permits an individual to perform certain acts and exercise certain rights, with or without any restrictions. e age of legal majority is the legally recognized threshold of adulthood. In general, age of legal majority issues can be subdivided into categories of contractual law, immigration, and citizenship issues regarding undocumented immigrants and criminal prosecution. e exact age of legal majority depends upon the jurisdiction and its application to the law. In the United States and many countries throughout the world, immigration authorities use the age of 18 for the legal age of majority. e contractual legal age of majority in most states and territories in the United States and Canada is also age 18 with the following exceptions: Age 14 in the American Soma; Age 19 in Alabama, Nebraska, British Columbia, New Brunswick, Newfoundland, Labrador, Northwest Territories, Nova Scotia, Nunavut, and Yukon; Age 21 in Mississippi and Puerto Rico (World Law Direct). Although the U.S. Supreme Court in the case of Roper v. Simmons, 2005, discontinues the death penalty for those committing a crime under the age of 18, the criminal prosecution of juveniles as an adult is possible. Again, this age depends upon the jurisdiction; however, as a general rule, the likelihood that a district attorney will be able to le criminal charges against a juvenile as an adult increases with the age of the juvenile, the severity of the oense and the juvenile’s past history of delinquency (Lewis and Brumit, 2011). With all of these legal issues that may require age assessment and the wide variety of ages applied depending upon jurisdiction and application, it would be wise for the forensic odontologist to become familiar with the laws within their own jurisdiction.
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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 | 0.021 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.016 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.021 | 0.023 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".