Bioarchaeological Sampling Strategies: Reflection on First Sampling Experience at the Templo Mayor Museum in Mexico City
Bibliographic record
Abstract
Given that sampling strategies and protocols in bioarchaeology are rarely discussed in the literature, this paper is an attempt at reflecting upon the skeletal sampling process (e.g., preparation period, development of strategies and protocols, decision-making process, collaboration with those involved) as well as provide some considerations that may be useful to other junior researchers carrying out their sampling within the realm of bioarchaeology (also may be applicable to other research fields that engage in sampling specimens from museum collections). I provide the considerations about human bone and teeth as it pertains to stable isotope analysis from the literature and then move to discuss my sampling process experience: the preparation period, the sampling process, and the sampling map I developed as an initial guide in the field. Finally, I discuss the main considerations I found helpful in the field which overall involve: 1) Familiarity with the skeletal collections; 2) Constant communication and participant collaboration with those involved in the process; 3) Establishing a feasible sampling protocol well-founded on research questions and biochemical analysis planned as a guide in the field but flexible and open to changes; 4) Handling administrative and logistical aspects of the process well in advance of the sampling visit, and 5) Continual awareness that while as researchers we value skeletal collections in a scientific manner, these also may have other kind of value to others so we must treat these collections with outmost respect at all times (i.e., when discussing, sampling, analyzing, interpreting, and disseminating our research).
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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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".