YOUTUBE TM INSIGHTS INTO MOOSE-TRAIN INTERACTIONS
Bibliographic record
Abstract
To gain a better understanding of the behavioral aspects of moose-train encounters, we reviewed videos of ungulate-train interactions available on YouTube TM and from train operators. Video footage consisted of 21 animal-train encounters including moose (Alces alces; 47.4%), cattle (Bos taurus; 15.8%), deer (Odocoileus spp.; 10.5%), elk (Cervus elaphus; 10.5%), camels (Camelus dromedarius; 10.5%), and sheep (Ovis aries; 5.3%). Footage was recorded predominantly in snow-free conditions, but most moose-train interactions were in winter when moose appeared to be trapped by deep snow banks along rail beds. Moose, elk, and deer all ran along the rail bed primarily inside of the tracks and nearer the rails than track center. Collision mortality generally occurred on straight stretches of track. Escapes occurred where a discontinuity in the habitat/setting occurred and/or when train speed was reduced. We suggest that videos can provide a valuable resource for interpreting ungulate reactions to trains and that videos gathered purposefully on railways and posted on open source databases will be useful for studying the dynamics of moose-train collisions for mitigation planning. ALCES VOL. 46: 183-187 (2010)
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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".