Birds and models: Not as different as you might think.
Bibliographic record
Abstract
For some time, the Sturdy laboratory group has been studying chickadee vocal production and perception using a variety of approaches. These include, among others, bioacoustic analyses of vocalizations, operant conditioning studies, and, more recently, artificial neural networks. This multidisciplinary approach has been very fruitful. The addition of artificial neural networks to the standard empirical approaches has significantly enhanced the understanding of songbird behavior and has provided models of bird operant conditioning behavior, perception, and cognition, allowed the investigation of questions that would be difficult to carry out with animal studies, honed research questions and foci, and has inspired further empirical studies. This talk will provide a longitudinal review of these and related research findings capitalizing on this data-model/model-data interplay. Topics discussed will include models of bird note type perception, models that have directed the formation of hypotheses about important perceptual features in note types, and models that have inspired further empirical studies of note type perception and have been used to explore a classic cognitive phenomenon, peak-shift, in a multimodal, note-type continuum.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.011 |
| Scholarly communication | 0.005 | 0.017 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.012 | 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".