Occupancy, detectability, and density of crab-eating fox (<i>Cerdocyon thous</i>) in two protected areas of restinga habitats in Brazil
Bibliographic record
Abstract
Crab-eating foxes (Cerdocyon thous (Linnaeus, 1766)) are frequently recorded in lists of mammal communities. However, studies quantifying aspects of the ecology of the species are uncommon in the literature. Thus, we aimed to quantify the density, activity, habitat use, and potential threats of C. thous in two protected areas (PAs) in the State of Espírito Santo, Brazil. We used data derived from camera traps and sand plots to model occupancy, detectability, activity; we also used random encounter models (REMs) to model density and abundance. We also estimated the activity of the species. Density of C. thous was 0.82 individuals/km2 with a total abundance of 119 individuals. We concluded that in the PAs studied, C. thous had bimodal, twilight–nocturnal activity patterns and was associated with water sources. Although the species in the area has a relatively high density compared with that from other areas in Brazil, it could be locally threatened by the highway that crosses the two PAs, promoting roadkill events, and by domestic dogs (Canis familiaris Linnaeus, 1758) recorded in these areas. Results presented herein can be a starting point to support future work in the region and to make predictions regarding the management and conservation of C. thous, a widely distributed species.
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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 | 0.000 |
| 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".