Demographic features and habitat preferences of southern pygmy mice (Baiomys musculus) in Colima, Mexico
Bibliographic record
Abstract
In Colima, Mexico, we conducted 8-night mark–recapture grid studies of southern pygmy mice ( Baiomys musculus (Merriam, 1892)) in January 2003–2007 in dry deciduous and tropical moist forests. For 40 000 trap-nights, 436 individuals were captured 764 times (comprising 18.6% of small mammals). The male:female ratio was 1:1.37, 82.9% were adults, 51.4% of females were pregnant or lactating, and coastal animals, on average, were heavier than those inland. Females moved farther than males. Baiomys was on 17 of 25 grids (densities 1.82–106.24 individuals/ha). Moon illumination did not affect capture or recapture probabilities, but trap-night did (overall, probability increased 1.66%/night). Grids with Baiomys had less bare ground and a more open canopy. Capture sites had more grass, less dead wood and bare ground, a more open and lower canopy, and greater distance to trees. Logistic regression indicated coastal capture sites had more forbs, grasses, and woody plants, and thicker vegetation at 1 m height with trees being relatively distant. Variables differed somewhat for northern sites, and the equation for all grids included 10 of 14 environmental variables. Baiomys frequented edge of an unimproved road traversing one grid. Baiomys musculus, widespread in Colima, exhibited preference for habitats with relatively dense ground cover and at least a partially open canopy.
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 | 0.000 |
| 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) | 0.001 | 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".