PRACTICAL ASPECTS OF THE IMPLEMENTATION OF THE EPIDEMIOLOGical SUPERVISION OF TRANSMITTED DISEASES IN AREAS OF RISK
Bibliographic record
Abstract
Golubyatnikov N. I., Bakhmutzan O. Yu., Borisenko O. I., Omaidze N. O. PRACTICAL ASPECTS OF THE IMPLEMENTATION OF THE EPIDEMIOLOGical SUPERVISION OF TRANSMITTED DISEASES IN AREAS OF RISK. ВІСНИК МОРСЬКОЇ МЕДИЦИНИ. 2019;2(83):7-11. ISSN 0049-6804. DOI http://dx.doi.org/10.5281/zenodo.3267013 http://www.herald.com.ua UDК 614.449 PRACTICAL ASPECTS OF THE IMPLEMENTATION OF THE EPIDEMIOLOGical SUPERVISION OF TRANSMITTED DISEASES IN AREAS OF RISK N. I. Golubyatnikov, O. Yu. Bakhmutzan, O. I. Borisenko, N. O. Omaidze State Enterprise ”Laboratory centre of Ministry of Health Care of Ukraine on the water transport” e-mail: nymba.od@gmail.com. Summary Considering that at the interstate borders of Ukraine, maritime entry points cooperate with subjects of different jurisdictions, shipping links cover epidemic dangerous countries of the world, the decision on disease vector surveillance and control in ports, airports and land transport hubs is seen in the proper implementation of the recommendations of the International Health Regulations (2005). In Ukrainian legislation, it is necessary urgently to develop guidelines and make them obligatory in the fight against dangerous infectious diseases that are transmitted by rodents. Key words: International Health Regulations (2005), Guidelines for Surveillance of Disease Vector Control and Fight at Ports, Airports and Ground Transportation Hubs, dangerous infectious diseases that are transmitted by rodents.
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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 | 0.024 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".