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Record W2214459069 · doi:10.1021/acs.estlett.5b00308

Real-Time Detection of Arsenic Cations from Ambient Air in Boreal Forest and Lake Environments

2015· article· en· W2214459069 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

VenueEnvironmental Science & Technology Letters · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicArsenic contamination and mitigation
Canadian institutionsUniversity of Toronto
FundersEuropean Research CouncilNorges ForskningsrådDivision of Graduate EducationTekesNordForskAcademy of Finland
KeywordsArsenicEnvironmental chemistryBiogeochemical cycleSnowmeltEnvironmental scienceTaigaChemistryBorealEcologySnowGeology

Abstract

fetched live from OpenAlex

We present the first observation of airborne organic and inorganic arsenic cations, detected in real time within the boreal forest in Hyytiälä, Finland, and over nearby Lake Kuivajärvi. The technique of atmospheric-pressure interface time-of-flight mass spectrometry provides online, in situ monitoring as well as chemical information about the arsenic species, identified as protonated trimethylarsine oxide (AsC 3 H 10 O + ) and AsO(H 2 O) n + clusters ( n = 0–4). Quantum chemical calculations confirm that the proposed cations are stable under atmospheric conditions. Our most remarkable discovery is that minimal arsenic appeared during spring 2011 until after the ground began to thaw, triggering a sharp increase in airborne arsenic levels as snowmelt flooded the soil with water and stimulated microbial activity. These findings reveal that volatile arsenic species, detected here as atmospheric ions, link the biogeochemical cycling of arsenic through air, soil, water, and living organisms.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.683
Threshold uncertainty score0.905

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 studies0.0000.002
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 head0.004
GPT teacher head0.191
Teacher spread0.187 · 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