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Record W2166574479 · doi:10.1029/2005gl025595

Wildfires threaten mercury stocks in northern soils

2006· article· en· W2166574479 on OpenAlexaffabout
Merritt R. Turetsky, J. W. Harden, H. Friedli, Mike Flannigan, Nicholas J. Payne, J.G. Crock, Lawrence F. Radke

Bibliographic record

VenueGeophysical Research Letters · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsCanadian Forest Service
Fundersnot available
KeywordsMercury (programming language)Environmental scienceBorealPeatWetlandMethylmercuryClimate changeSoil waterTaigaEnvironmental chemistryEcologyGeologySoil scienceGeographyOceanographyBioaccumulationForestryChemistry

Abstract

fetched live from OpenAlex

With climate change rapidly affecting northern forests and wetlands, mercury reserves once protected in cold, wet soils are being exposed to burning, likely triggering large releases of mercury to the atmosphere. We quantify organic soil mercury stocks and burn areas across western, boreal Canada for use in fire emission models that explore controls of burn area, consumption severity, and fuel loading on atmospheric mercury emissions. Though renowned as hotspots for the accumulation of mercury and its transformation to the toxic methylmercury, boreal wetlands might soon transition to hotspots for atmospheric mercury emissions. Estimates of circumboreal mercury emissions from this study are 15‐fold greater than estimates that do not account for mercury stored in peat soils. Ongoing and projected increases in boreal wildfire activity due to climate change will increase atmospheric mercury emissions, contributing to the anthropogenic alteration of the global mercury cycle and exacerbating mercury toxicities for northern food chains.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.243
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.002

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 head0.031
GPT teacher head0.312
Teacher spread0.281 · 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.

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

Citations148
Published2006
Admission routes2
Has abstractyes

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