Bibliographic record
Abstract
Risk assessments utilising the consolidated risk assessment process as described by Public Safety Canada and the Centre for Security Science utilise the five threat categories of natural, human accidental, technological, human intentional and chemical, biological, radiological, nuclear or explosive (CBRNE). The categories of human intentional and CBRNE indicate intended actions against specific targets. It is therefore necessary to be able to identify which pieces of critical infrastructure represent the likely targets of individuals with malicious intent. Using the consolidated risk assessment process and the target capabilities list, coupled with the CARVER methodology and a security vulnerability analysis, it is possible to identify these targeted assets and their weaknesses. This process can help emergency managers to identify where resources should be allocated and funding spent. Targeted Assets Risk Analysis (TARA) presents a new opportunity to improve how risk is measured, monitored, managed and minimised through the four phases of emergency management, namely, prevention, preparation, response and recovery. To reduce risk throughout Canada, Defence Research and Development Canada is interested in researching the potential benefits of a comprehensive approach to risk assessment and management. The TARA provides a framework against which potential human intentional threats can be measured and quantified, thereby improving safety for all Canadians.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".