ASSESSING AVALANCHE RESCUE DOGS' ABILITY TO DETECT HUMAN SCENT WITH CONTAMINANTS ON SITE: DEVELOPING A METHODOLOGY
Bibliographic record
Abstract
ABSTRACT: Avalanche search and rescue dogs are efficient in locating buried humans in avalanche debris when alternative rescue techniques fail. It is poorly understood how surface contaminants affect a dog's scenting ability at the search site. In this small-scale pilot study we developed a practical and re-peatable methodology to test dogs ’ scenting abilities amidst various surface contaminants including tree debris and gasoline fumes. The tests, conducted over the course of two days with operational dogs from Alaska Search and Rescue Dogs and Alyeska Ski Patrol, included two control and two contaminant tests on 30-m by 30-m simulated avalanche debris fields. Quantitative analysis reveals the time taken to indi-cate human scented articles was significantly higher with the introduction of tree debris (p <.001) while the time was significantly lower with the introduction of gasoline fumes (p <.001). Further video analysis was used to assess dogs ’ working behavior around contaminants. The results provoke questions as to whether tree debris acts as a scenting or visual distraction and why an introduction of gasoline fumes does not adversely affect dog’s scenting ability. Using the developed methodology, we will collect more data during winter season 2014-15 from operational dog teams throughout the U.S and Canada. Analysis of contaminants effects on scenting abilities will provide handlers insight into avalanche dogs ’ scenting abilities and help enhance the training of avalanche rescue dogs.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".