Bibliographic record
Abstract
It may seem unusual to suggest a relationship between Leonardo Da Vinci (1452–1519) and the study of avian communication, as he is perhaps better known for his attempts at developing a theory of human flight based on the principles of avian flight. His approach, however, was that “from the study of structure comes the knowledge of function.” Here, I present how an understanding of the structure of the various vocalizations produced by chickadees may lend to an understanding of the function of these vocalizations. Chickadees are an excellent model system for this type of research given that they produce various calls that are comprised of individual units that may function in different ways. We have conducted several bioacoustics analyses in the search for similarities and differences among call structures, as well as attempted to delineate the bioacoustical markers that would provide meaningful information to listeners. Further, I will discuss what constitutes human “language” and how the calls of chickadees may satisfy the criteria for a non-human language. With this in mind, we use various field and laboratory techniques in an attempt to understand the structure of vocalizations which may in turn convey information regarding the function of the vocalizations.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".