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Record W4361285127 · doi:10.1007/s13280-023-01855-y

Global change effects on biogeochemical mercury cycling

2023· review· en· W4361285127 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAMBIO · 2023
Typereview
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsWilfrid Laurier UniversityUniversity of Ottawa
FundersHorizon 2020 Framework ProgrammeNational Institute of Environmental Health SciencesDartmouth CollegeGulf Research ProgramNational Science Foundation
KeywordsBiogeochemical cycleMercury (programming language)Environmental scienceGlobal changeCyclingBiomeEnvironmental changeEcosystemPermafrostClimate changeEnvironmental chemistryEnvironmental protectionEcologyGeographyChemistryBiology

Abstract

fetched live from OpenAlex

Past and present anthropogenic mercury (Hg) release to ecosystems causes neurotoxicity and cardiovascular disease in humans with an estimated economic cost of $117 billion USD annually. Humans are primarily exposed to Hg via the consumption of contaminated freshwater and marine fish. The UNEP Minamata Convention on Hg aims to curb Hg release to the environment and is accompanied by global Hg monitoring efforts to track its success. The biogeochemical Hg cycle is a complex cascade of release, dispersal, transformation and bio-uptake processes that link Hg sources to Hg exposure. Global change interacts with the Hg cycle by impacting the physical, biogeochemical and ecological factors that control these processes. In this review we examine how global change such as biome shifts, deforestation, permafrost thaw or ocean stratification will alter Hg cycling and exposure. Based on past declines in Hg release and environmental levels, we expect that future policy impacts should be distinguishable from global change effects at the regional and global scales.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.980
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.012

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 head0.097
GPT teacher head0.369
Teacher spread0.271 · 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