The <i>Xenopus</i> retinal ganglion cell as a model neuron to study the establishment of neuronal connectivity
Bibliographic record
Abstract
Neurons receive inputs through their multiple branched dendrites and pass this information on to the next neuron via long axons, which branch within the target. The shape the neuron acquires is thus the key to its proper functioning in the neural circuit in which it participates. Both axons and dendrites grow in a directed fashion to their target partner neurons by responding to a large number of molecular cues in the milieu through which they extend. They then go through the process of synaptogenesis, first choosing a neuron on which to synapse, and then the appropriate subcellular location. How a neuron acquires its unique shape, establishes and modifies appropriate synaptic connectivity, and the molecular signals involved, are key questions in developmental neurobiology. Such questions of nervous system wiring are being pursued actively with a variety of different animal models and neuron types, each with its own unique advantages. Among these, the developing retinal ganglion cell (RGC) of the South African clawed frog, Xenopus laevis, has proven particularly fruitful for revealing the secrets of how axons and dendrites acquire their final morphology and connectivity. In this review, we describe how this system can be used to understand the multiple molecular events that instruct the incorporation of RGCs into the neural circuit that controls vision.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".