Camera trap survey of medium and large mammals in a montane rainforest of northern Peru
Bibliographic record
Abstract
Camera traps are a powerful tool for inventorying elusive and rare species and very useful to obtain ecologi- cal data for plans that involve wildlife conservation. In Peru, several surveys have been carried out in lowland Amazonia especially in the southeastern part of the country, but none in montane cloud forests or Yungas. We present the first camera trap studies produced in Peruvian Yungas at the locality of Querocoto village (Chota, Cajamarca), based on 2002 (dry season) and 1264 (wet season) camera traps-days (CTD). Two localities were surveyed in wet and dry season: The Pagaibamba Protection Forest and the San Lorenzo Forest. The wet season study was carried out in October and November, and the dry season in July to September of 2008. Eight mammalian species were recorded in both seasons. Some 66 (91.7%) independent records were obtained in the dry season, but only six (8.3%) in the wet one, suggesting a seasonality effect. The Mountain Paca Cunicu- lus taczanowskii was the most commonly photographed species, with 17.0 and 1.6 capture frequencies (dry and wet season respectively), whereas the Long-tailed weasel Mustela frenata (0.5 capture frequency in the dry season) was the most rare species. Activity patterns suggest that Mountain Paca C. taczanowskii and the Andean Skunk C. chinga are nocturnal, while Spectacled Bear T. ornatus and Tayra E. barbara are diurnal in the study area. Our records of the Ocelot Leopardus pardalis and the Tayra E. barbara are among the highest altitudinal records known for each species. In addition, the Anta Tapirus pinchaque was also identified by its tracks, representing one of the first record known south of the Huancabamba Depression.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".