The Use of an Orthotic Casting Foam as a Track‐Plate Medium for Wildlife Research and Monitoring
Bibliographic record
Abstract
Track plates are an inexpensive, non‐intrusive and widely used wildlife monitoring tool. Almost all track‐plate media are not suited for use during rainy conditions, because rain or heavy dews can distort tracks or render the medium ineffective for registering further animal visitations. Furthermore, available track‐plate media may also produce tracks of varying quality and hard‐surfaced media produce inaccurate two‐dimensional imprints. We experimented with the utility of using Biofoam™, a phenolic foam used in orthotics, as a track‐plate medium. Domestic animals were utilized to produce track imprints on track plates constructed of casting foam, coarse sand, fine sand, very fine sand and sooted aluminum. Resulting tracks were photographed and images were used in a questionnaire which was completed by expert wildlife trackers who rated the quality of the tracks. Tracks in the casting foam are three‐dimensional, register claws, clearly depict all pads, accurately record shape and size, and convey very fine anatomical details. Track imprints on the casting foam were rated consistently better by experts than track imprints left on the other media. Field trials demonstrated that the casting foam and tracks in the casting foam can endure lengthy periods of inclement weather conditions in the field without being damaged or distorted. In summary, where three‐dimensional, high‐quality prints need to be recorded in variable field conditions, we recommend the use and further experimentation with track plates made of casting foam.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".