Bibliographic record
Abstract
Detecting infiltration across national borders is not simply a matter of deploying commercial-off-the-shelf perimeter intrusion sensors. The sheer length of border systems has led some to propose using wide area surveillance systems to reduce cost. Unfortunately the most common of these technologies: thermal infrared and visible wavelength sensors integrated with video analytics and ground radar have line-of-sight limitations and less than optimum nuisance alarm characteristics for real world border applications. The missing link is a cost-effective terrain following trip-wire sensor to cue these wide-area systems and mitigate their performance limitations. Buried ported coax, sometimes called leaky coax or guided radar sensors have protected high value perimeters for over two decades. In theory, their high probability of detection, resistance to defeat and vandalism, invisible terrain following volumetric field and good nuisance alarm characteristics make them well suited to secure borders; but their high cost per zone and inability to accommodate different soil conditions have argued against their use. OmniTraxtrade is a new ultra wideband spread spectrum ranging guided radar which changes this equation with a lower cost per zone, one-meter target resolution and the ability to accommodate different soil types. Ongoing research may eventually result in the ability to track targets along the cables, determine the direction of travel of targets crossing the cables, surface mount the sensor cables in rocky terrain and cost effectively install the sensor cables in soil or sand using cable plows; any of which would only improve the technology's utility in border intrusion detection.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.867 | 0.816 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".