Bibliographic record
Abstract
Sensors for outdoor perimeter detection must reliably detect intruders under all possible intrusion threat scenarios while rejecting environmental stimuli including nearby human activity. For the outdoors a wide variety of detection principles are possible in order to provide a measurable signal of an intruder's presence. Generally algorithms implemented in firmware are employed to differentiate the human characteristics from those of a changing environment. This paper is an update of an earlier paper presented in the 1990s that outlines the how's and why's of field testing employed to both develop outdoor sensors and also prove their detection performance in the intended environment. This paper also shows the results of testing various sensor types, and highlights the sensor limitations and the pitfalls of testing. Finally, the use of a test site to develop sensor control and display systems, or investigate novel sensor applications, is discussed. The test environment described is called SITE (Sensor Integrated Test Environment) located near Carp, Ontario, Canada. At this facility, virtually all existing all-weather outdoor sensor technologies, from buried line cables, through microwaves, passive IR, and barrier-attached sensors; taut-wire, and linear microphonic cables are deployed. These sensors are installed in typical operational configurations for assessment or comparison. Fortunately, at this facility environmental stimuli ranges from temperatures of -40/spl deg/C to +35/spl deg/C, with snow and freezing rain, and summer lightning. As a rural site, an assortment of wildlife, from groundhogs to deer, are typical nuisance alarm stimuli. In this environment, human and mechanical targets are used to assess the detection of real threats. To better address the environmental performance of sensors, a new element of sensor integration is also reviewed, namely the incorporation of video and weather data in the control and display component of a security facility. This assists operators in making a rapid assessment, or maintenance personnel for sensor adjustments. This technology is also employed in the sensor testing phase to help define the best implementation, and as a useful SITE testing tool.
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 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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".