Intelli-FIELD/sup TM/ the next generation, electrostatic field disturbance sensor
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
Outdoor perimeter volumetric field disturbance sensors must reliably detect perturbations to the field caused by an intruder, while rejecting noise and environmental changes that may be orders of magnitude greater than the target response. Currently, E-Field/sup (R)/ systems are widely deployed in nuclear, correctional and industrial sites to provide perimeter security. These systems are effective in rejecting the majority of noise and environmental stimuli through combined fixed attribute threshold comparison techniques. However, some environmental stimuli closely mimic target stimuli, so improved discrimination techniques have been sought. The paper describes the results of current studies and investigations of electrostatic sensor system response to targets and to various environmental changes. Fundamental principles in the character of sensor response to these varied stimuli are discussed. Techniques and methods that may be used to exploit the difference between intruder and environmental responses, while using cost-effective discrimination methods, are described. It is shown bow the new Intelli-FIELD system was created, using currently available technologies, to provide both excellent detection properties, and an extremely low nuisance alarm rate, while at the same time greatly simplifying installation, calibration and maintenance. The details of the new system hardware components and test results from initial field installations are described. A comparison of field performance with the previous E-Field product is provided to indicate the advantages of this new sensor technology.
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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