A Tale of Tails: The description and potential function of tail-flagging behaviours in Eurasian red squirrels (Sciurus vulgaris)
Bibliographic record
Abstract
Not only do animals communicate with one another, but they also demonstrate the use of mixed communication strategies. One such strategy, multimodal communication, involves multiple sensory signals used together to communicate messages. For example, an animal may use visual signals, auditory signals, or a combination of both, to communicate. Although multimodal communication has been observed in many animal species, there is still a surprising lack of data. Many studies focus on either a single aspect of the multimodal signal or on the joint signal alone. However, information about multimodal signals, as well as each component unimodal signal, are needed in order to understand and categorize the purpose of joint messaging. Our study aims to address this gap in the literature by investigating multimodal communication in Eurasian red squirrels. Since Eurasian red squirrels use visual and acoustic vocal signals in both joint and independent contexts, they provide an excellent opportunity to study the specific circumstances in which multimodal communication occurs. Additionally, because there is minimal research on Eurasian red squirrels, this study will provide preliminary investigations into how this endangered species communicates and navigates the world around it. Department: Psychology Faculty Mentor: Dr. Shannon Digweed
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".