Personnel Detection at a Border Crossing—An Exercise
Bibliographic record
Abstract
Abstract : In March 2012, an international team of scientists, engineers, and technicians gathered at the southwest border of the United States with specialized equipment to collect data on people, animals, and vehicles travelling in the rugged terrain. The goal of the effort is to collect data in the natural environment and develop robust algorithms to detect people, animals, and vehicles with fewer false alarms and high confidence. The Canadian team used SASNet; the Israeli team used Pearls of Wisdom; University of Memphis brought a Profiling sensor; and the University of Mississippi, Night Vision and Electronic Sensors Directorate, the Space & Naval Warfare Systems Command (SPAWAR), and U.S. Army Research Laboratory brought their equipment to collect the data. Representatives from Finnish Defense participated in observing the team. Some of the sensor modalities used are acoustic, seismic, passive infrared (IR), profiling sensor, sonar, and visible and IR imaging sensors. Some description of the sensors and their data analysis is presented. In this report, we present the data collection effort and some of the algorithms developed for various sensor modalities along with the results on the field data.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".