Bibliographic record
Abstract
The use of ion channels to control defined events in defined cell types at defined times in the context of living tissue or whole organism represent one of the major advance of the last decade, and optogenetics (i.e the combination of genetic and optical methods) obviously played a key role in this achievement.(1) Although the existence of light-activated ion channels (i.e ospin channels) has been known since 1971,(2) it took about 35 y before the concept of an ion channel used for bioengineering control of cell or tissue activity becomes reality.(3) From that moment forward, rhodopsine channels(4) (,) (5) (i.e blue light-gated non-specific Na(+) channels that depolarize cells thus increasing cell excitability) or halorhodopsin channels(6) (i.e yellow light-gated Cl(-) channels that hyperpolarize cells thus decreasing cell excitability) have been extensively used to turn neurons on and off in response to diverse colors of light, with an extremely high temporal precision (i.e milliseconds range). Although optogenetics has been originally established in neuroscience, it addresses now to non-neuronal systems, including cardiac, smooth and skeletal muscles, glial cells or even embryonic stem cells.(7) (-) (9) However, although light stimulation allows control of cell excitability with a high spatio-temporal specificity, light waves present the disadvantage to not penetrate deep tissue, and implanted devices are required for in vivo light stimulation. In contrast to visible light-waves, radio-waves (i.e longer wavelength and lower frequency) can penetrate deep tissues with minimal energy absorption.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.009 |
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".