Role of Landscape Scale in the Distribution of Rodents in an Agroecosystem of Argentina
Bibliographic record
Abstract
The goal of this study was to assess the effect of the different habitats on rodent diversity, and to estimate the effect of changes in land use on the rodent abundance through different possible scenarios. We sampled poultry farms, human houses, riparian habitats, railway embankments, woodlots, pasture, crop fields and their borders. The habitats with highest frequency of captures were poultry farms and crop field borders, mainly because of Mus musculus and Akodon azarae captures, respectively. All rodent species were found in at least six of the nine habitats sampled, but in some of them with low frequency. The different habitats differed in their contribution to the abundance of each species. Crop fields and pasture borders contributed more than 40% to the abundance of A. azarae, Oxymycterus rufus, Oligoryzomys flavescens and Calomys musculinus, while poultry farms had higher abundance of M. musculus. Woodlots and railway embankments showed a high contribution to O. flavescens abundance. The increase in the area covered by crop fields and human habitats led to an increase in the abundance of M. musculus and Calomys spp. and to a decrease in the relative abundance of other species. Considering the role of habitat diversity in rodent diversity, our results suggest that none of the species studied, except M. musculus, which is highly dependent on farms, depends on a single habitat and that their abundance is supported by a variety of less perturbed habitats. The current changes in land use would generate an increase in M. musculus abundance in detriment of wildlife species which are associated with undisturbed habitats.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".