A comprehensive survey of mammal collections and genetic resources in South America: challenges and directions
Bibliographic record
Abstract
Abstract Natural history collections serve as crucial infrastructure for both basic and applied scientific research, providing temporal and spatial specimen data needed to understand biodiversity, environmental change, and emerging pathogens. This study surveyed mammal collections across South America to assess the scope and quality of this infrastructure. A detailed questionnaire was distributed to curators and collection managers from May 2021 to February 2022, gathering information on institutional characteristics, collection size, taxonomic and geographical scope, preservation methods, genetic resource availability, percentage digitization, financial support, and challenges such as funding limitations. Our survey identified 141 collections; more than twice the number reported by the American Society of Mammalogists in 2018. South American collections house ∼746 000 catalogued specimens, including 452 primary type specimens, representing only a modest proportion of the vast mammalian diversity of South America. Collections are geographically concentrated in Argentina, Brazil, Colombia, and Peru, with a significant gap in the Guianas region and a decline in responses from Venezuela. The survey highlights four major challenges facing South American collections: staffing shortages, minimal cryogenic infrastructure, incomplete digitization, and sustainability issues. This initiative aims to raise awareness of collections in South America, plan for strategic growth, and strengthen research capacity to address pressing global issues, such as climate change, zoonotic disease transmission, and long-term conservation strategies.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".