Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Hair, body, and pubic lice plagued past populations just as much as they do today. These types of lice require a human host to survive, and they thrive in contact-rich and sedentary groups. Lice, especially head lice, are difficult to get rid of without constant attention, which makes them suitable as proxy data for studying human behaviours of the past. By studying lice in the archaeological record, archaeologists can further understand the human experience. For instance, lice, eggs, and delousing combs have been found with human remains in the archaeological record and have been collected, cleaned, and studied, to better understand the lives of past humans. Additionally, body lice can spread diseases and can indicate stressors people endured during life and prior to death, such as overcrowding and illness. Lice studies have also been used to evaluate human cultural behaviours, how people interacted with others, how people lived with lice (if and where delousing activities took place), and how people dealt with ongoing infestations in the past. This article serves to provide a comprehensive overview of the archaeological analysis of lice, the important insights that lice can bring to current understanding of the past, the importance of proper collection, cleaning, and studying of lice, and the ways in which lice in the archaeological record have informed archaeologists about the past.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it