Non-Invasive Survey of Forest Carnivores in the Northern Cascades of Oregon, USA
Bibliographic record
Abstract
Several species of forest carnivores are of state or federal concern in Oregon and are or may be sensitive to timber management practices, wildfires, climate change, and other large-scale disturbances. We implemented a non-invasive survey of forest carnivores in the northern Cascades of Oregon during fall–spring, 2012–2014. We collected 111,148 images from 21 elevated and 39 ground-level baited camera stations located from 586 to 2237 m in elevation. We detected (≥1 image) Red Fox (Vulpes vulpes) at 9 ground stations, of which 4 also detected Coyote (Canis latrans). We detected American Marten (Martes americana) at 63% of all stations (elevation range = 1252–2237 m), including 5 of 7 stations located in areas that experienced wildfires since 1996 that covered >5000 ha. Other forest carnivores detected included Bobcat (Lynx rufus), Black Bear (Ursus americanus), Mountain Lion (Puma concolor), Northern Raccoon (Procyon lotor), American Mink (Neovison vison), weasel (Mustela spp.), and skunk (Mephitis mephitis, Spilogale gracilis); but we did not detect Wolverine (Gulo gulo), Canada Lynx (Lynx canadensis), Fisher (Martes pennanti), or Gray Wolf (Canis lupus). Future periodic non-invasive surveys of forest carnivores may provide information about changing species composition and distribution, especially in relation to climate change, vegetation succession, and potential recolonization by Gray Wolves.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".