Bibliographic record
Abstract
Hydra belongs to the class Hydrozoa in the phylum Cnidaria. Hydra is a model animal whose cellular and developmental data are the most abundant among cnidarians. Hence, I discuss the developmental neurobiology of hydra. The hydra nerve net is a mosaic of neural subsets expressing a specific neural phenotype. The developmental dynamics of the nerve cells are unique. Neurons are produced continuously by differentiation from interstitial multipotent stem cells. These neurons are continuously displaced outwards along with epithelial cells and are sloughed off at the extremities. However, the spatial distribution of each neural subset is maintained. Mechanisms related to these phenomena, i.e., the position-dependent changes in neural phenotypes, are proposed. Nerve-net formation in hydra can be examined in various experimental systems. The conditions of nerve-net formation vary among the systems, so we can clarify the control factors at the cellular level by comparing nerve-net formation in different systems. By large-scale screening of peptide signal molecules, peptide molecules related to nerve-cell differentiation have been identified. The LPW family, composed of four members sharing common N-terminal L(or I)PW, inhibits nerve-cell differentiation in hydra. In contrast, Hym355 (FPQSFLPRG-NH3) activates nerve differentiation in hydra. LPWs are epitheliopeptides, whereas Hym355 is a neuropeptide. In the hypostome of hydra, a unique neuronal structure, the nerve ring, is observed. This structure shows the nerve association of neurites. Exceptionally, the tissue containing the nerve ring shows no tissue displacement during the tissue flow that involves the whole body. The neurons in the nerve ring show little turnover, although nerve cells in all other regions turn over continuously. These associations and quiet dynamics lead me to think that the nerve ring has features similar to those of the central nervous system in higher animals.
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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 | 0.001 |
| 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".