Litter size and seasonality in reproduction for Guianan rodents and opossums
Bibliographic record
Abstract
François M. Catzeflis*a http://orcid.org/0000-0002-7810-557X, Burton K. Limb http://orcid.org/0000-0002-0884-0421 & Claudia Regina Da Silvac http://orcid.org/0000-0002-3280-0235a Institut des Sciences de l’Evolution, Université de Montpellier, CNRS, IRD, EPHE, Montpellier, Franceb Department of Natural History, Royal Ontario Museum, Toronto, ON, Canadac Laboratório de Mamíferos, Instituto de Pesquisas Científicas e Tecnológicas do Estado do Amapá, Macapá, Amapá, BrazilCONTACT François M. Catzeflis francois.catzeflis@univ-montp2.frABSTRACTWe studied the litter sizes of small rodents and opossums caught in the Guianan Region (Brazilian Amapá, French Guiana, Suriname, and Guyana) by pooling the data of animals collected during various field trips conducted primarily between 1990 and 2017. A series of 569 counts of embryos (or of pouch young for marsupials) in 40 species of Didelphidae (N = 12 species), Sigmodontinae (18), Murinae (2), and Echimyidae (8) allowed for a more detailed characterization of the reproductive condition of 14 species known each by more than 10 pregnant females. For eight species with at least 20 pregnant females, an examination of seasonality in breeding occurrence documented that the two months with the lowest percentage of pregnant females are July and August (16.0 and 17.3%, respectively) during the end of the long wet season and beginning of the dry season. By contrast, January and February showed the highest abundance of pregnancies (57.9% and 55.8%, respectively) during the beginning of the long wet season. This timing coincides with most juveniles foraging during the height of the wet season in May when food is presumably most prevalent.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".