Bibliographic record
Abstract
Between 1750 and 1930 certain doctors, anatomists and anthropologists strived to enrich their collections with the skeleton of a giant human. These skeletal remains are authentic bones of human beings that grew to reach an enormous height, not the false remains of mythical humans. For decades, no one offers a rational explanation for such a huge growth, although hypotheses were raised at the end of the 19th century that end up being endorsed somewhat later. The article reviews the circumstances associated with this collecting and mentions the cases of giant human skeletons that are preserved (or have been until recently preserved) in anatomical and anthropological museums in Europe, the United States and Canada Entre mediados del siglo XVIII y la década de 1920 ciertos médicos, anatomistas y antropólogos se afanan por enriquecer sus colecciones con el esqueleto de un humano gigante. Son los huesos auténticos de seres humanos que crecieron hasta alcanzar una altura desaforada, no los presuntos (y falsos) restos de personajes anclados en la leyenda. Durante décadas, nadie ofrece una explicación racional de tan enorme crecimiento, aunque ya a finales del XIX se plantea alguna hipótesis que acabará siendo refrendada algo después. El artículo revisa las circunstancias asociadas a este coleccionismo y comenta los casos conocidos de esqueletos de gigantes humanos que se conservan (o se han conservado hasta hace pocos años) en museos anatómicos y antropológicos de Europa, Estados Unidos y Canadá
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".