The Andronowski Skeletal Collection for Histological Research: A Modern Anatomical Contribution
Bibliographic record
Abstract
The Andronowski Skeletal Collection for Histological Research (ASCHR) comprises the fastest-growing documented modern human skeletal collection in the world developed specifically for histological and imaging research. Initiated in 2017 by Dr. Janna M. Andronowski, the ASCHR provides a resource for the study of skeletal microarchitectural variability with advancing age and between the sexes. The primary objective is to use this unique skeletal archive for histological and imaging research, with the goal of furthering knowledge of human bone biology. Bone procurement has focused on two sites commonly used in histological age-at-death estimation in anthropology: the mid-shaft sixth rib and femoral mid-shaft. The ASCHR consists of over 1200 bone samples from 621 individuals and thousands of imaging files, with age-at-death ranging from 15–105 years. Additional information collected about ASCHR donors includes occupational history; alcohol, tobacco, and drug use history; a health questionnaire; and cause and manner of death. The ASCHR offers a novel opportunity to devise regression formulae for histological age-at-death estimation and answer questions concerning age-related microarchitectural changes and biomechanical processes. It further serves as a skeletal reference database for researchers from various disciplines, including medicine, anthropology, and the biological sciences. Here, we describe the background of the collection, ethical considerations, bone procurement processes, demographic composition, and existing imaging and histological data available to researchers. Our primary aims are to (1) introduce the scientific community to ASCHR, (2) present descriptive and demographic information regarding the collection, and (3) encourage collaboration among national and international researchers interested in human skeletal biology.
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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 | 0.019 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.009 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.038 | 0.030 |
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".