Occupational Medicine and Environmental Health in the Border Areas of Euro-Arctic Barents Region: A Review of 30-Year Russian–Norwegian Research Collaboration Outcomes
Bibliographic record
Abstract
This year marks 30 years of close collaboration between a consortium of institutions, namely, the Northwest Public Health Research Center, Saint-Petersburg (NWPHRC); the Institute of Community Medicine (ICM) of UiT (The Arctic University of Norway, Tromsø); the National Institute of Occupational Health (NIOH), Oslo; the University Hospital of Northern Norway (UNN) at Tromsø; and McMaster University (MU), Hamilton, ON, Canada. During the early years of cooperation, Dr. Chashchin was the Director of the Scientific Laboratory of the North-West Public Health Centre Branch of the NWPHRC located in the town of Kirovks in the Murmansk Region. The primary focus of this long-standing collaboration was to assess and address issues that are important for maintaining the health of the population living in the border areas of Russia and Norway and included the reduction of occupational health risks among workers employed in mining, metallurgical and machine-building enterprises located in the north-western region of Norway and the adjoining Kola Peninsula in Russia. These industrial activities constituted essential components of the local industries. The ongoing Russian-Norwegian cooperation in the field of occupational medicine is an excellent example of the effective combination of intellectual potential and research technologies of multiple countries. It has resulted in the development of a scientifically based set of measures for practical implementation, contributing to the improvement of working conditions and preservation of the health of workers employed at enterprises where the joint research was carried out.
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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 | 0.034 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".