Palaeoproteomic identification of breast milk protein residues from the archaeological skeletal remains of a neonatal dog
Bibliographic record
Abstract
Accurate postmortem estimation of breastfeeding status for archaeological or forensic neonatal remains is difficult. Confident identification of milk-specific proteins associated with these remains would provide direct evidence of breast milk consumption. We used liquid chromatography coupled to tandem mass spectrometry (MS) to confidently identify beta-lactoglobulin-1 (LGB1) and whey acidic protein (WAP), major whey proteins associated with a neonatal dog (Canis lupus familiaris) skeleton (430-960 cal AD), from an archaeological site in Hokkaido, Japan. The age at death of the individual was estimated to be approximately two weeks after birth. Protein residues extracted from rib and vertebra fragments were analyzed and identified by matching tandem MS spectra against the dog reference proteome. A total of 200 dog protein groups were detected and at least one peptide from canine LGB1 and two peptides from canine WAP were confidently identified. These milk proteins most probably originated from the mother's breast milk, ingested by the neonate just before it died. We suggest the milk diffused outside the digestive apparatus during decomposition, and, by being absorbed into the bones, it partially preserved. The result of this study suggests that proteomic analysis can be used for postmortem reconstruction of the breastfeeding status at the time of death of neonatal mammalian, by analyzing their skeletal archaeological remains. This method is also applicable to forensic and wildlife studies.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".