Thinking of Co<sup>2+</sup>‐staining explant tissue or cultured cells? How to make it reliable and specific
Bibliographic record
Abstract
Ca(2+) and/or Zn(2+) entry into neurons and glial cells is often a key step driving the processes of neurodevelopment and disease. As a result, a major pre-occupation of many neuroscientists has been in tracking down when and where nervous tissues express ion channels with appreciable divalent ion permeability. The cobalt (Co(2+))-staining technique is one of the few techniques that allow a snapshot of the entire neuronal circuit, and selectively labels cells expressing divalent-permeable ion channels with a brown-black precipitate. Despite this, its use has been remarkably limited in the past decade. Reluctance to employ this approach has largely been related to an earlier concern with obtaining a reliable and reproducible means of visualizing transported Co(2+). Here we show that recent advances have resolved these issues, opening this straightforward and valuable technique to a much larger neuroscience audience.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.007 |
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".