MétaCan
Menu
Back to cohort
Record W2085893167 · doi:10.1029/2003eo360002

Ocean‐atmosphere‐sea ice‐snowpack interactions in the Arctic, and global change

2003· article· en· W2085893167 on OpenAlexaffabout
P. B. Shepson, P. Matrai, L. A. Barrie, J. W. Bottenheim

Bibliographic record

VenueEos · 2003
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsEnvironment and Climate Change Canada
FundersNational Science Foundation
KeywordsBiogeochemical cycleArcticOzone depletionSea iceEnvironmental scienceSnowpackOceanographyCryosphereAtmosphere (unit)Atmospheric sciencesHazeArctic geoengineeringPolar nightSunriseClimatologySnowGeologyGeographyChemistryMeteorologyEnvironmental chemistryAntarctic sea iceStratosphere

Abstract

fetched live from OpenAlex

The discovery of the nature of Arctic haze in the late 1970s and early 1980s [Barrie, 1986] showed that the Arctic is not a pristine environment isolated from human activity, but rather, a region well connected to natural and anthropogenic sources of chemicals by winds, ice movement, and marine currents. Copious pollution is carried on the winds to the Arctic during winter and spring from Europe and northern Asia. The study of this phenomenon led serendipitously to the discovery of ozone depletion chemistry in the Arctic marine boundary layer (MBL) at polar sunrise [Oltmans, 1981; Bottenheim et al., 1986]. In turn, research to understand surface ozone depletion chemistry led to the discovery that it is perturbing the biogeochemical cycle of many elements such as mercury; and that ozone depletion chemistry is likely to have a significant impact on radiative transfer in the atmospheric layer near the surface, with important consequences on the air‐sea exchange of biologically‐mediated compounds.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.086
Threshold uncertainty score0.170

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.026
GPT teacher head0.245
Teacher spread0.218 · 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

Citations25
Published2003
Admission routes2
Has abstractyes

Explore more

Same venueEosSame topicAtmospheric chemistry and aerosolsFrench-language works237,207