Mercury Risk Evaluation, Risk Management and Risk Reduction Measures in the Arctic (ARCRISK) – Inception Report
Bibliographic record
Abstract
The project “Risk evaluation, risk reduction and risk management action plans for mercury in the Arctic – a circumpolar management approach” (ARCRISK) has been developed to address mercury pollution in the Arctic. The main objective is to develop an action plan with targeted risk reduction measures for mercury releases from key sources to land and water in the Arctic. The action plan will cover key sources from each of the four selected case study river catchment basins in Canada (1), Norway (1), and Russia (2). The ARCRISK project team consists of experts from nine highly skilled research institutes, universities and other institutions, from Canada, Norway, Russia and USA. An inception workshop was held in Oslo in March 2020 to consolidate the team and collectively develop the project framework. The present report summarizes key deliberations and decisions made as part of the inception phase, to further operationalize the project, making detailed plans and decisions for implementation of the project in 2020-2022. In addition to the components described in the inception report, the framework also includes an updated budget and a workplan for implementation of WP2-6.
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 | 0.021 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".