Collaborative Point Paper on Border Surveillance Technology
Bibliographic record
Abstract
Background. Controlling movement across national borders presents numerous political, economic and technical challenges. While primary responsibility for border control rests with the US Department of Homeland Security (DHS) and Public Safety and Emergency Preparedness Canada (PSEPC), the respective defense departments have both key roles and significant materiel resources with which to support national security objectives. Given the size and in many cases remoteness of North American borders, technical solutions are necessary to act as multipliers to limited manpower within government enforcement agencies. The products and technologies to tackle this effort reside not only in the private sector, but in capabilities either under development or already in use by the military. Civil agencies can benefit from leveraging these capabilities if hurdles such as cost, training, interoperability and security can be overcome. 3. Purpose. The purpose of a collaborative point paper is to: (1) provide information regarding a subject of interest to both defense departments, (2) identify on-going technology and procurement activities within that subject area, (3) identify subject mailer experts and organizational representatives in both departments with responsibility for the subject area, and (4) advocate collaboration using either the NATIBO MOU or another appropriate agreement. The point paper uses, in part, the format for a NATIBO working group Terms of Reference (TOP) document to facilitate the establishment of a formal working group under the MOU. 4. Objectives. The primary objective is to establish collaborative efforts on Border Surveillance technologies, system development, demonstration/test, and deployment concepts. The collaboration could include, but not be limited to, studies on technologies or requirements, joint research initiatives, technology transfer/insertion demonstrations,
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.033 | 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; both teacher heads 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".