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Diversity and strength of ice-related dimethyl sulfide sources in the Arctic

2020· article· en· W3042392362 on OpenAlexaffabout
Maurice Levasseur, Martine Lizotte, Virginie Galindo, Margaux Gourdal, Michel Gosselin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsUniversité du Québec à RimouskiUniversité Laval
Fundersnot available
KeywordsDimethyl sulfideCryosphereArctic ice packEnvironmental scienceSea iceArcticSnowContext (archaeology)Atmospheric sciencesOceanographyClimatologyPhysical geographySulfurChemistryGeologyMeteorologyGeography

Abstract

fetched live from OpenAlex

Biogenic sources of sulfur are important precursors of aerosols in the Arctic during the summer months. Recent studies show that peaks in ultrafine particle formation events often coincide with hotspots of dimethyl sulfide (DMS) emissions from the marginal ice zone. During the last 10 years, we explored the diversity of DMS sources associated with the ice and at the marginal ice zone in the Canadian Arctic, and assessed how the projected changes in sea ice extent, thickness, and other properties could strengthen or weaken these emissions. Results from four Arctic expeditions presenting DMS concentrations and dynamics in snow, sea ice, melt ponds, under-ice water, and at the ice edge will be shown and discussed in the context of ongoing and future changes in the cryosphere. The analysis of the pooled dataset points toward an increase in DMS emissions in a warmer Arctic with a potential cooling feedback on climate.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.321
Threshold uncertainty score0.639

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.016
GPT teacher head0.185
Teacher spread0.170 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2020
Admission routes2
Has abstractyes

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