Natural Disasters: Epidemics, Pandemics and Use of Armed Forces in Support of Risk Management
Bibliographic record
Abstract
Epidemics or pandemics are configured as events corresponding to a biological risk, which can be of natural or anthropogenic origin, intentional or involuntary, which cause a great impact on the population, showing the vulnerability of the human being. Thus, the States, governments and various entities that confrontt, eventually with the support of international organizations and NGO's, seek to provide a timely response to reduce the uncertainty and fear that exists in the citizenry. Thereby, part of the institutions that support the first response is the military power, which with its mobilization, logistics, leadership, command and control, among other capacities, provides its human resources, goods and equipment to face these threats, which in addition to being a problem of public health, can become a problem for the security, peace and stability of a State. And although in all countries the Defense sector is considered to strengthen the response, there are similarities and differences regarding their employ, also identifying the importance of preparing and synchronizing plans between civil and military entities to improve their capabilities by time to attend emergencies of this type. Finally, the importance of taking care of military personnel who will carry out support missions to other State entities is considered, where physical and psychological health will avoid diminishing the aid capacity, as well as training activities, organization and generation of doctrine, to have ready units that collaborate effectively and do not hinder actions, especially in functions that go beyond the activities of protection, isolation and physical security.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".