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Record W1978851845 · doi:10.1002/2013eo330002

Studying Bromine, Ozone, and Mercury Chemistry in the Arctic

2013· article· en· W1978851845 on OpenAlexaff
S. V. Nghiem, P. Clemente‐Colón, Thomas A. Douglas, C. W. Moore, Daniel Obrist, Donald K. Perovich, Kerri A. Pratt, Ignatius Rigor, William R. Simpson, P. B. Shepson, A. Steffen, John E. Woods

Bibliographic record

VenueEos · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsEnvironment and Climate Change Canada
FundersCalifornia Institute of TechnologyJet Propulsion LaboratoryNational Aeronautics and Space AdministrationNational Oceanic and Atmospheric AdministrationNational Science Foundation
KeywordsSea iceArctic ice packArcticArctic geoengineeringOceanographyAntarctic sea iceArctic sea ice declineMercury (programming language)SnowCryosphereDrift iceEnvironmental scienceGeologyClimatologyAtmospheric sciencesGeomorphology

Abstract

fetched live from OpenAlex

Accentuated by a new record low in 2012, the springtime extent of Arctic perennial sea ice continues its precipitous decline. Consequently, the Arctic sea ice cover is increasingly dominated by seasonal sea ice, consisting of thinner and saltier ice with more leads (fractures), polynyas (areas of open water), nilas (sea ice crust less than about 10 centimeters thick), frost flowers (clusters of salty ice crystals on sea ice surface), and saline snow. The increase in the salinity of the sea ice cover is potentially conducive to ice‐mediated photochemical and meteorological processes leading to ozone (O3) and gaseous elemental mercury depletion from the atmosphere.

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)0.000
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.016
GPT teacher head0.238
Teacher spread0.222 · 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

Citations33
Published2013
Admission routes1
Has abstractyes

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