Utilizing a Multi-Technique, Multi-Taxa Approach to Monitoring Wildlife Passageways on the Bennington Bypass in Southern Vermont
Bibliographic record
Abstract
Roadways affect wildlife habitat disproportionate to the area of land they occupy while impacting wildlife directly through direct loss of habitat, road mortality and disruption of movement. Roadways indirectly impact wildlife by isolating populations and disrupting gene flow and metapopulation dynamics. A variety of strategies have been used with mixed success to mitigate the impacts of transportation systems on wildlife. Underpasses are commonly used to facilitate movement of wildlife across roadways in Europe, Australia, Canada and the U.S.Through 2005, 460 terrestrial and 300 aquatic crossing structures have been identified throughout the United States but only a small portion of these crossings have monitoring incorporated into their project design. Most monitoring is limited to usage of the passage structures with little data collected on movement through the adjacent landscape. Monitoring of the passage structures helps determine wildlife use of the structures but is limited in the ability to determine landscape level impacts.A variety of techniques are utilized in monitoring passageway effectiveness, primarily camera traps and track beds. Building on prior studies, the Bennington Bypass project takes a broad, multi – taxa approach to monitoring crossing structures on a newly constructed highway in southern Vermont. We are utilizing a variety of techniques to assess movements of an array of species at the passage structure and in the surrounding landscape.Techniques utilized in our study include: small mammal trapping, track beds/plates, remote camera sensing, snowtracking, road kill surveys, roadside track beds, amphibian recording devices, snake pit tagging and observational studies. We are also using this broad approach to monitoring as an opportunity to test and refine many of the techniques used in the study. By monitoring a wide variety of animal movements rather than focusing exclusively on wildlife use of the passages, we expect to more accurately assess the effectiveness of the mitigation structures. We anticipate that the results from this work will assist in developing monitoring protocols for future studies in Vermont and throughout the United States.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".