Bibliographic record
Abstract
Anemia AmongPast Maya Populations: When Will We Have the Answer?Today it has been estimated that approximately 2.15 billion people worldwide are iron deficient, and almost 1.20 billion people suffer from various severities of iron-deficiency anemia (Wright & Chew 1999: 925).Anemia has also been a serious problem for several populations in the past, including those of the ancient Maya civilization.Although we cannot know for sure the actual prevalence of the disease among the ancient populations, with the available skeletal analysis from the osteoarchaeological record we can at least infer that it was a health problem for the Maya, as it is for the world's populations of today.The Maya civilization flourished from approximately 900 BC to AD 900 with populations occupying areas including Guatemala, Honduras, Mexico and Belize.There have been many studies devoted to the rise and fall of the Maya, and almost every other aspect of their daily lives.However, studies on the prevalence of various diseases among the Maya have not been researched as much as their importance would warrant and, beyond this, the various causes or reasons for the prevalence of so many health related ailments have not been adequately researched.With the exception of diseases that the Spanish mayor may not have inadvertently transmitted, such as small pox and syphilis, discussion of ailments that were not immediately life threatening, or infectious were generally ignored.This paper will examine the various models that attempt to explain the high prevalence of iron deficiency anemia among the ancient Maya civilization, as well as possible faults inherent in each.'II JTF \ r "(,1 1., 2!JfI+ ,'.1)' IS C')l"'n~hr <' YOS TOTF\!: Th, 1.'\\;0 .!'lUm:ll d' .\""hropd,;.;'•
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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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.012 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".