Cleveland neural engineering workshop 2017: strategic evaluation of neural engineering
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 Cleveland Neural Engineering Workshop (NEW) was established as a biennial meeting in 2011, with subsequent meetings taking place in 2013, 2015, and most recently, June 2017. This fourth biennial NEW was hosted by the Cleveland Advanced Platform for Technology National Veterans Affairs Center, the Functional Electrical Stimulation National Veterans Affairs Center, the Biomedical Engineering Department at Case Western Reserve University in Cleveland, Ohio, and Northwell Health's Feinstein Institute for Medical Research of New York. The workshop connects leaders and stakeholders in the neural engineering community who are devoted to developing and deploying technological solutions to those with neurological disorders. The meeting in 2017 continued strategic conversations initiated at the third Cleveland NEW conference in 2015. The goal of the 2017 workshop was to was to determine specific actions by which the neural engineering community might advance the goals outlined in 2015, assess progress towards that plan, adjust as necessary, and establish continued strategic direction. This meeting report summarizes the outcomes.
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 | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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