IDENTIFICATION OF ANDEAN FELID FECES USING PCR-RFLP
Bibliographic record
Abstract
Scat analysis is a useful method to determinedistribution, abundance and diet of animals(Putman, 1984; Kohn and Wayne, 1997;Wasser et al., 2004). These methods are par-ticularly relevant for monitoring elusive andsecretive carnivores for which feces are oftenthe only available materials (Foran et al., 1997;Riddle et al., 2003). As feces of similar-sizedcarnivore species can resemble each other inmorphology and composition, distinguishingamong them can be problematic (Davidson etal., 2002; Prugh and Ritland, 2005). Develop-ment of methods for identification of the spe-cies from which feces originated is crucial forscat collection-based monitoring programs.DNA methods on scat use intestinal cells ofthe animal that are incorporated into feces.Molecular scatology has been demonstratedto be an efficient method to identify carnivorespecies in a large number of publications,including felids (e.g. Farrel et al., 2000; Ernestet al., 2000; Palomares et al., 2002; Wan et al.,2003; Zuercher et al., 2003). However, no pro-tocol specific to Andean felids has been pub-lished. Several projects focussing on Andeancat (
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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 | 0.002 |
| 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 teacher head, 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".