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Record W3102924966

Mercury Risk Evaluation, Risk Management and Risk Reduction Measures in the Arctic (ARCRISK) – Inception Report

2020· article· en· W3102924966 on OpenAlexaboutno aff
Cathrine Brecke Gundersen, Hans Fredrik Veiteberg Braaten, Eirik Hovland Steindal, S. Jannicke Moe, E. V. Yakushev, Guttorm Christensen, Jane L. Kirk, Holger Hintelmann, Natalia Frolova, Petr Terentjev, Sarah J. Roberts

Bibliographic record

VenueDuo Research Archive (University of Oslo) · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsnot available
Fundersnot available
KeywordsMercury (programming language)Risk managementRisk assessmentEnvironmental scienceRisk analysis (engineering)BusinessComputer science
DOInot available

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.161
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.057
GPT teacher head0.298
Teacher spread0.241 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2020
Admission routes1
Has abstractyes

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Same venueDuo Research Archive (University of Oslo)Same topicMercury impact and mitigation studiesFrench-language works237,207