The need to implement better integrated and reasonably optimized protection strategy – One Protection
Bibliographic record
Abstract
We are surrounded by various hazards and threats to the health of people, animals and the environment. Therefore, the protection work is multifaceted and needs to consider all potential hazards, and balance all potential impacts to the extent practicable. Whilst separated sectorial protections have proved capable of delivering adequate levels of protection for various risks we are facing, it is crucially important that protective activities of various sectors and disciplines be better integrated into a more holistic and reasonably optimized protection strategy – One Protection. Even though the holistic approach has been in practice for decades, more has to be done for better integration with the aim to achieve net health and societal benefits “As Optimal As Reasonably Achievable”. In this paper, radiological protection is taken as an example to discuss challenges when sector-specific protective activities are not well integrated and lack prioritization and optimization. It demonstrates the need for better integrated and reasonably optimized protective practices to achieve the objectives of One World One Health and the UN Sustainable Development Goals.
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 | 0.044 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.016 | 0.019 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.007 | 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".