FINGERPRINT VERIFICATION FOR CONTROL OF ELECTRONIC BLAST INITIATION ABSTRACT
Bibliographic record
Abstract
In the current context of heightened concerns with explosives security, there is significant interest in technological controls to improve security. It is important to be able to control what is fired, by whom, where and when. This paper describes research Orica has performed to investigate and test biometric systems to address the question of "by whom". The goal of this research is to incorporate the most suitable biometric system onto the 'blaster ' unit of an electronic initiation system. This approach will ensure that only authorized personnel can initiate a blast involving electronic detonators. Requirements analysis: we initially explored many different biometric technologies to evaluate them against the requirements, including security, usability, ruggedness, size, form factor, privacy, and operational temperature range, This analysis identified chip based fingerprint sensors as the best candidate. Development of prototype units: in order to test the identified sensors, we modified standard, commercially-available, electronic blast initiation units ("blaster") to incorporate a fingerprint reader. Testing and evaluation: Biometric We conducted a biometric scenario evaluation in order to determine: 1) security level (measured by false accept rate (FAR)); 2) usability (measured by failure to enroll (FTE) and false reject rates (FRR)), and to 3) discover environment specific issues and challenges (such as temperature, humidity, dirt, or those related to the usage patterns of the user group). Tests were conducted at quarry sites in eastern Ontario, Canada. Results show rates of: FAR = 0%, FTE = 1.67%, FRR = 28.81%. Overall, these results suggest that this fingerprint biometric technology has a good level of usability in this application of electronic blast initiation control.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".