AMAZONIAN SMALL MAMMAL ABUNDANCES IN RELATION TO HABITAT STRUCTURE AND RESOURCE ABUNDANCE
Bibliographic record
Abstract
Previous studies in tropical rain forests suggest that most small mammal species reach their highest densities in disturbed habitats; however, only a few sites have been examined. Consequently, habitat and resource use for many species is poorly understood. This is especially true in the Amazon Basin, where no studies of microhabitat associations of small mammals have been undertaken. We studied relationships with habitat variables and resource abundances for 5 species of marsupials and 9 species of rodents at a site in southeastern Amazonia. Small mammals were sampled with traps placed both on the ground and in the understory. Eight habitat variables were measured to quantify habitat structure. Measures of insect biomass were collected by the use of sticky traps, and fruit abundance was quantified. Patterns of habitat use were examined using logistic regression, multiple regression, and ordinations. Many species showed increased abundances with habitat features indicative of edge-affected or disturbed habitats, showing negative relationships with understory openness, understory woody-stem density, tree density, and tree size; and positive relationships with number of vines per tree, mean log size, number of logs, and volume of downed wood. We obtained support for the hypothesis that the cause of this pattern is increased resource abundances in these areas, because both insect biomass and number of fruiting trees showed similar relationships. However, for many species, measures of resource abundance were not important once habitat features were entered into the models, indicating that the relationship to resources is an indirect one.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".