Bibliographic record
Abstract
Many different detection technologies have been employed for perimeter detection to sensitize a barrier, including for example, strain-sensing taut wire sensors, electric fences, electrostatic sensors, and various linear "microphonic" cable-sensing devices. These outdoor perimeter fence detection sensors must reliably detect intruders attempting to cut or climb the barrier, while ignoring the effects of environmental noise including nearby activity. In a recent conference proceedings, the new IntelliFIBER fiber optic based product was introduced and compared with previous technologies. We outline the advancements in the IntelliFIBER development since introduction, as well as, the field test results obtained from sensor testing each of the different options. Some of the new IntelliFIBER advancements are in the sensing cable options. These include, for example, a hybrid cable version with both embedded power conductors and additional fibers. This feature provides a highly robust cable, one that does not require a conduit for all-weather detection, while providing an economic advantage for multiple zone perimeter applications. With this option, both the power system and data communications are secured, and the expense of adding separate perimeter power and data networks is removed. This advancement provides for further applications beyond the typical perimeter one, such as, securing data or power networks from intrusion. Field test results, from our own outdoor field test S.I.T.E., are presented for the different cable options, and also compare IntelliFIBER with its triboelectric-based counterpart, Intelli-FLEX. The long-term monitoring data includes the actual performance, in terms of probability of detection, false and nuisance alarm rates. Vulnerability to defeat is also discussed.
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.
How this classification was reachedexpand
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 | 0.004 |
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 teacher head, 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".