<scp>SiMPL</scp> Wildlife Magnets: A Camera Trap Tool for Detecting All Creatures Great and Small
Bibliographic record
Abstract
ABSTRACT We created the SiMPL wildlife magnet—a baited camera trap design that allows passive monitoring of wildlife, particularly small‐ to medium‐sized mammals. The SiMPL wildlife magnet is inexpensive and easy to construct. To evaluate its effectiveness, we conducted a two‐year case study using 9 camera stations along an elevation gradient in the White Mountains of the northeastern United States. We examined how the detection probability of mammal species changed with the inclusion of a SiMPL wildlife magnet using data from pre‐and post‐establishment. We found a significant increase in community‐level detection probability with the use of SiMPL wildlife magnets and for individual species, including red squirrels ( Tamiasciurus hudsonicus ), American marten ( Martes americana ), and fisher ( Pekania pennanti ). Moreover, we were able to capture more species with SiMPL wildlife magnets than without, including flying squirrels ( Glaucomys spp.), various rodents ( Cricetidae spp.), black bears ( Ursus americanus ), moose ( Alces alces ), owls, and other birds. The SiMPL wildlife magnet is an effective, low‐cost method for surveying wildlife communities, especially rodents and mesocarnivores. It addresses the limited field of view presented by other techniques for capturing small mammals on camera traps and enables efficient collection of phenology data, including vegetation and snowpack. This tool has several applications, including monitoring species' responses to management practices and global change.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".