Organization of meissner corpuscles in the glabrous skin of monkey and cat
Bibliographic record
Abstract
Reconstructing neural-population responses in the form of spatial event plots assumes that the receptors are organized in a dense linear array. We have found that this assumption is not valid by determining the spatial organization of Meissner corpuscles (MCs) in the glabrous skin of both cat and monkey. The tissue was excised from animals that had been cardiac perfused with 4% paraformaldehyde. One-micrometer plastic sections revealed that the morphology of these receptors is different in the two animal species. However, in both species, they reside in approximately the same place in the dermal pegs of the skin, between the epidermal ridges, and electrophysiologically they both respond to ramp-and-hold stimuli with a rapidly adapting firing pattern. Thus, in this study we will refer to the receptors of the cat as "Meissner-like". In monkey, MCs are located in the dermal papillae between the epidermal limiting and intermediate ridges, forming orderly rows, the contours of which follow the overlying fingerprint. Although the average density of MCs is 45/mm2, they are distributed along the dermal pegs in such a manner as to give rise to three significantly ( p < 0.017) different average distances between corpuscles. We note that "fingerprints" vary in topography across the hand and this is also reflected in the underlying MC arrays. In the cat, these "Meissner-like" receptors display no specific pattern and have a density much lower than in the monkey. Cat glabrous skin does not have "fingerprints". The results emphasize that the spatial organization of tactile receptors must be taken into account when interpreting reconstructed population responses.
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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 | 0.000 | 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) | 0.001 | 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".