Building Linux based neural network applications
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
Neural networks can be trained to approximate arbitrary nonlinear mappings. Because of this capability, they have been successfully used in applications such as system modeling, time-series prediction, automatic control and pattern recognition. In these applications, a mapping is needed to represent the input-output relationship of a real-world process. Neural networks can be trained to form this mapping. However, process parameters may vary over time. When this occurs, the neural network has to be retrained. If a neural network is already being used in a system, new real-time data has to be collected and used to retrain the neural network. Data collection and retraining have to be conducted without disturbing the main task. The retraining should be automatically initiated when significant errors are detected and should stop when the new neural network is satisfactory. Developing the software for such a neural network based system is not trivial, especially if the application is for embedded systems. The development can be made easier when a multitasking operating system such as Linux is employed. This paper provides the results of the investigation into how such an adaptive system can be designed.
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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 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