Hair-Snares: A Non-Invasive Method for Monitoring Felid Populations in the Selva Lacandona, Mexico
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Bibliographic record
Abstract
Non-invasive techniques such as hair snares have been used in conjunction with molecular methods to study species that occur at low densities and have elusive behavior, as an alternative to invasive methods such as trapping and hunting. This study was designed to evaluate the use of hair snares as a non-invasive method for the collection of felid and other mammalian samples in the tropical rainforest of the Selva Lacandona, Chiapas, Mexico. Hair snares were placed along transects in Montes Azules Biosphere Reserve for four months a year in 2005 and 2006. Hairs were selected based on morphological characteristics and identification of species was done based on a diagnostic portion of mtDNA cytochrome b region. A total of 389 hits on 888 hair-snare checks were recorded, representing a capture rate of 43%. The species identified included margay ( Leopardus wiedii, n=2), ocelot ( Leopardus pardalis, n=1), jaguarundi ( Puma yagouaroundi, n=1), gray fox ( Urocyon cinereoargenteus, n=1), tayra ( Eira barbara, n=3), coati ( Nasua narica, n=1), four-eyed opossum ( Metachirus nudicaudatus, n=6), and common opossum ( Didelphis marsupialis, n=16). The present study is the first to report the successful collection of hair samples from jaguarundi and margay in the wild and hair samples from ocelots in tropical areas. The deficit of information on carnivore populations in tropical rainforests is due mainly to the lack of appropriate methodologies that are reliable and cost-effective. This study supports the assumption that hair-snaring is viable and cost-effective in ecosystems such as the Selva Lacandona, particularly when monitoring carnivore populations that have wide geographic distributions and low densities.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it