Small Mammal Prey Base for American Marten (Martes americana) within the Manistee National Forest of Michigan
Bibliographic record
Abstract
American martens (Martes americana) are typically found in late-successional forests with closed canopy cover and high structural diversity. Reintroduced populations of martens in the Northern Lower Peninsula of Michigan inhabit areas that are devoid of many of these features, which may impact their prey base. The goal of our study was to evaluate the small mammal prey base available to martens in the Northern Lower Peninsula. To assess prey availability, diversity, and composition, as well as effects of trap type and habitat features on trapping success, we sampled 24 study plots within the Manistee National Forest for small mammals in 2013 (n=24) and 2014 (n=20). Study plots were situated in four habitat types: conifer, deciduous, mixed conifer-deciduous, and mixed oak. Total capture rates were significantly and positively associated with relative deciduous tree cover. Furthermore, this result highlights how managing tracts of land for small mammal prey base may overlap with goals set forth by researchers for marten habitat needs (e.g., resting site preferences). We found large Sherman traps had significantly higher total capture rates than other trap types (medium Sherman and pitfall traps), and we recommend that researchers use a variety of trap types to maximize detectability of small mammal species diversity and richness.
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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 | 0.001 | 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) | 0.002 | 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".