Preparing for Receiving EMT Following a Major Earthquake in Israel
Bibliographic record
Abstract
Background/Introduction: Israel is prone to a major earthquake along the Jordan Valley which will have a devastating impact on Jordanians, Palestinians, and Israelis. Magen David Adom Israel partnered with other national societies and is working together with the Israeli authorities to prepare for such an eventuality. Objectives: To describe the processes of the preparation work done in Israel, identify the main results, and lessons learnt. Method/Description: The activities followed this methodology: A mapping exercise of the main issues to be addressed across different sectors. Series of joint multi-sectoral workshops with the relevant stakeholders to discuss the issues identified. Formulating the respective SOPs of the different stakeholders. Series of joint multi-sectoral workshops with the relevant stakeholders to discuss the issues identified. Formulating the respective SOPs of the different stakeholders. Results/Outcomes: Main results: Dedicated immigration procedures Dedicated customs procedures, including import permits including customs clearance and transport to the operations site. Health EoC to be tasked with handling arriving EMT and monitoring their operation. EMT to be embedded into existing Israeli health care facilities. Overcoming gaps in pain medication (controlled substances) and new generation antibiotics. Working with hospitals expected to receive an EMT on a joint deployment SOP Conclusion: While many issues identified in the processes were resolved with many positive outcomes, there is still a list of other issues still pending decisions, which are in the future work plan. In order to sustain the results, and ensure their validity over time, an ongoing cooperation, nationally and with the international partners, including simulations, is essential.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".