HUNTER, JOHN; SIMPSON, BARRIE; COLLS, CAROLINE STURDY. FORENSIC APPROACHES TO BURIED REMAINS. 1. ED. OXFORD: WILEY BLACKWELL. 2013, 259 P. IL. COLOR.
Bibliographic record
Abstract
Forensic Approaches to Buried Remains, editado pela John Wiley & Sons, foi escrito a três mãos por John Hunter, professor do Instituto de História Antiga e Arqueologia da Universidade de Birmingham, que desenvolve estudos sobre a Arqueologia Forense desde 1988. Publicou mais de 10 livros sobre a relação entre a Arqueologia e as Ciências Forenses e vários artigos e capítulos de livros. Em coautoria com Barrie Simpson, um ex-oficial de investigação sênior e um ativo médico forense, expert na análise de locais de crime e no uso de abordagens interdisciplinares, e Caroline S. Colls, uma especialista em Arqueologia Forense e do Conflito, professora de Ciências Forenses da Universidade de Staffordshire, Hunter aprimora as suas mais recentes observações sobre o tema, publicadas com Roberts e Martin em 2002 (Studies in Crime: A introduction to Forensic Archaeology) e com Margaret Cox em 2006 (Forensic Archaeology – Advances in theory and practice). Convém notar que Hunter, além de estar comprometido com trabalhos de pesquisa arqueológica em todo o Reino Unido, tendo realizado atividades de campo na Bósnia, no Iraque e em Falklands, rotineiramente ministra palestras para policiais e profissionais forenses. Foi um dos fundadores do Forensic Search Advisory Group, validando o atual sistema de atuação da Arqueologia Forense no Reino Unido.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.019 | 0.013 |
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".