Microchiropterans have a diminutive cerebral cortex, not an enlarged cerebellum, compared to megachiropterans and other mammals
Bibliographic record
Abstract
Small echolocating bats are set apart from most other mammals by their relatively large cerebellum, a feature that has been associated to echolocation, as it is presumed to indicate a relatively enlarged number of neurons in the cerebellum in comparison to other brain structures. Here we quantify the neuronal composition of the cerebral cortex, cerebellum and remaining brain structures of seven species of large Pteropodid bats (formerly classified as megachiropterans), one of which echolocates, and six species of small bats (formerly classified as microchiropterans), all of which echolocate. This chiropteran data is compared to 60 mammalian species in our dataset to determine whether the relatively large cerebellum of the small echolocating bats, and possibly that of the echolocating Pteropodid, contains a relatively enlarged number of neurons. We find no evidence that the distribution of neurons differs between microchiropterans and megachiropterans, but our data indicate that microchiropterans, like the smallest shrew in our dataset, have diminutive cerebral cortices, which makes the cerebellum appear relatively large. We propose that, in agreement with the diminutive brain size of the earliest fossil mammals, this is a plesiomorphic, not a derived, feature of microchiropteran brains. The results of this study also reveal important neural characteristics related to the phylogenetic affinities and relationships of the chiropterans.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".