Techniques for Integrating Macrobotanical and Microbotanical Datasets: Examples from Pre-Hispanic Northwestern Honduras
Bibliographic record
Abstract
What are useful quantitative approaches in situations with highly variable data quantities, contexts, and sampling strategies? How can paleoethnobotanical findings be interpreted without over-representing data or selling results short? Described here are several major issues and potential solutions. The four sites of the study are located northwestern Honduras, a region with fairly hostile environments for paleoethnobotanical preservation. For this reason, several types of botanical residues are combined to provide a more holistic picture of past ethnobotanical practices. In some cases, these data prove to be complementary, while in others, they are corroborative. This article includes tactics for integrating multiple sample protocols, multiple and overlapping diagnostic elements, multiple and overlapping clade categories, multiple and overlapping samples in a single locus, multiple and overlapping formation processes, and multiple and overlapping cultural practices. In each section, the issue, sampling strategies, quantitative approaches, and a few results are described.
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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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".