Principles of an intraneural wire electrode array signal measurement device
Bibliographic record
Abstract
The measurement technique discussed within this paper has an array electrode sensors placed through the cross-section of an intact ventral root nerve. Neural potentials above a threshold value are to be recorded in the region of each electrode. This paper's scope is limited to the design of a prototype nerve cuff and instrumentation as it applies to the usefulness of a micro-machined intraneural wire electrode array as a measurement/characterization technique. Data obtained from the 37 channel device will be analyzed by a neural network. Potentially, muscular movement will be correlated with real time neural data. A micro-machined nerve cuff will be fitted with an array of Pt electrodes deposited on tines. The tines project from the cuff into the nerve. The cuff will have a 1 mm length and a 1 mm diameter. Two half-cylinders will fit together to form the cuff. The intraneural wire electrode array signal measurement device is designed to record 37 channels (regions) of neural activity. Analog sensor channels will form the data input domain and one digital channel forms the output data domain. Each sensor channel's input is to be amplified, filtered, sampled, counted and displayed. Each stage of the instrumentation functions to provide a uniform characterization of neural data.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".