High tech aviation security program in africa - a model for technology transfer
Bibliographic record
Abstract
This paper focuses on the US Department of Transportation's (DOT) international airport security initiative in Nigeria. Recently, the aviation leadership of Africa, recognizing the need to take aviation security to a higher level, has been working with DOT under the Nigeria Transportation Project (NTP) since 1998. The NTP initiative, a special Nigerian Airport Security Program has been implemented since 2001 using various security technologies as test beds and program enhancements to aviation security. The impact of these technologies are to ensure that Nigerian Airport Security Program will be brought to compliance with existing and post 911 International Civil Aviation Organization (ICAO) security standards and regulations. The concept of intermediate technologies and mature technologies transfer to emerging countries will be described. The critical security issues facing the aviation security in the uses of technologies, their operations and training will be covered. The technology transfer concept with the uses of a test-bed approach will be detailed. The security technologies covered include perimeter security, passenger screening, immigration/passport document screening, carry-on baggage screening, checked baggage screening and cargo screening. The current, on going and new initiatives on various security measures such as passenger-baggage tracking and reconciling, radio-frequency identification device (RFID) technology, access controls etc., are also be outlined. Also, training using ICAO's aviation security training packages (ASTP), and best practices gained through on-site computer-based training (CBT) of screeners for all related ICAO-type classroom courses, carried out in the NTP are described. Complexity of training of screeners/supervisors for checkpoints, that mirror US checkpoints, taking into account religious, language and political differences, which made this task challenging, are addressed. These include technician training; screener and supervisor training for: X-ray machine - carry-on baggage, checked baggage and cargo, primary and secondary walk-through metal detector, explosive trace detectors (ETD), passenger passport/visa identification/verification systems etc. Insight gained and successes on the deployed security equipment from both technology and process points of view are covered. The impact to aviation security and airports program in specific and to the Federal Airport Authority in Nigeria (FAAN) in general are also addressed. Finally, the inter-governmental technology transfer and intra-governmental lessons learned are summarized.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".