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Record W2520299343 · doi:10.1002/2015jd024114

Influence of mountains on Arctic tropospheric ozone

2016· article· en· W2520299343 on OpenAlexafffundabout
J. A. Seabrook, J. A. Whiteway

Bibliographic record

VenueJournal of Geophysical Research Atmospheres · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsYork University
FundersCanadian Space Agency
KeywordsTroposphereOzoneArcticEnvironmental scienceTropospheric ozoneAtmospheric sciencesSpring (device)ClimatologyAir mass (solar energy)The arcticOceanographyMeteorologyGeologyGeography

Abstract

fetched live from OpenAlex

Abstract Tropospheric ozone was measured above Ellesmere Island in the Canadian Arctic during spring of 2008 using a differential absorption lidar. The observations were carried out at Eureka Weather Station, which is located between various mountain ranges. Analysis of the observations revealed that mountains had a significant effect on the vertical distribution of ozone. Ozone depletion events were observed when air that had spent significant time near to the frozen surface of the Arctic Ocean reached Eureka. This air arrived at Eureka by flowing over the surrounding mountains. Surface level ozone depletions were not observed during periods when mountains blocked the flow of air from over the sea ice. In the case of blocking there was an enhancement in the amount of ozone near the surface as air from the midtroposphere descended in the lee of the mountains. Three case studies from spring of 2008 are described.

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 score0.387
Threshold uncertainty score0.769

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 communication0.0010.000
Open science0.0000.000
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 head0.023
GPT teacher head0.289
Teacher spread0.265 · 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

Citations13
Published2016
Admission routes3
Has abstractyes

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