Quantitative approaches to sensory information encoding by bat noseleaves and pinnae
Bibliographic record
Abstract
The biosonar systems of horseshoe bats (Rhinolophidae) and Old World round leaf-nosed bats (Hipposideridae) incorporate a pervasive dynamic at the interfaces for ultrasound emission (noseleaves) and reception (pinnae). Changes in the shapes of these structures alter the acoustic characteristics of the biosonar system and could hence influence the encoding of sensory information. The focus of the present work is on approaches that can be used to investigate the hypothesis that the interface dynamic effects sensory information encoding. Mutual information can be used as a metric to quantify the extent to which the different ultrasonic emission and reception characteristics (beampatterns) provide independent views of the environment. Two different quantitative approaches have been taken to evaluate the relationship between dynamically encoded additional sensory information and sensing performance in finding the direction of a biosonar target. The first approach is to determine an upper bound on the number of different directions that can be distinguished by virtue of distinct spectral signatures. The second approach is based on a lower bound (Cramér–Rao) on the variance of direction estimates. All these different metrics demonstrate that the peripheral dynamics seen in bats result in the encoding of additional sensory information that is suitable for enhancing biosonar performance.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".