Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 it