A new method to discriminate the deer mouse (<i>Peromyscus maniculatus</i>) from the white-footed mouse (<i>Peromyscus leucopus</i>) using species-specific primers in multiplex PCR
Bibliographic record
Abstract
Morphometric measurements or biochemical methods are often required to differentiate deer mice, Peromyscus maniculatus (Wagner, 1845), from white-footed mice, Peromyscus leucopus (Rafinesque, 1818), particularly when they are found in sympatry. However, these approaches cannot easily be applied to juveniles, or to degraded or ancient museum specimens. In this paper, we propose a rapid and non-invasive molecular approach to discriminate these cryptic species from one another. This technique relies on species-specific primers designed in the COIII mitochondrial region to amplify fragments of different lengths in each species. The method developed proved useful for the identification of ethanol-preserved, frozen, degraded, or dry museum specimens.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".