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Record W2138289208 · doi:10.1029/2005gl023032

Measurements of O<sub>3</sub>, NO<sub>2</sub> and Temperature during the 2004 Canadian Arctic ACE Validation Campaign

2005· article· en· W2138289208 on OpenAlexafffundabout
Tobias Kerzenmacher, Kaley A. Walker, Kimberly Strong, R. Berman, P. F. Bernath, C. D. Boone, J. R. Drummond, H. Fast, A. Fraser, Keith MacQuarrie, C. Midwinter, Keeyoon Sung, C. T. McElroy, R. L. Mittermeier, J. M. Walker, Hongjiang Wu

Bibliographic record

VenueGeophysical Research Letters · 2005
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsUniversity of WaterlooUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Space AgencyCanadian Foundation for Climate and Atmospheric Sciences
KeywordsRadiosondeArcticEnvironmental scienceOccultationAtmospheric sciencesMeteorologyLidarClimatologySatelliteGeologyRemote sensingGeographyOceanographyPhysics

Abstract

fetched live from OpenAlex

The 2004 Canadian Arctic ACE Validation Campaign was conducted to provide correlative data for validating measurements from the Atmospheric Chemistry Experiment (ACE) satellite mission. These measurements were made at Eureka, Nunavut during polar springtime 2004. Six ground‐based instruments were operated during the intensive phase of the campaign and ozonesondes and radiosondes were flown. During this time, ACE‐FTS and ACE‐MAESTRO were performing solar‐occultation measurements over the Canadian Arctic. We report the first comparisons between campaign measurements and those from ACE, focusing on O 3 , NO 2 and temperature. Initial mean O 3 profiles from ACE‐FTS and ACE‐MAESTRO agree to within 20% between 10 and 30 km, and the NO 2 profiles agree to within 40% between 17 and 40 km, which is within the standard deviations. The ACE‐FTS temperature profiles agree to better than 2.5 K with the radiosonde temperatures from 10 to 32 km and with the lidar temperatures from 17 to 45 km.

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 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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.672
Threshold uncertainty score0.976

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.021
GPT teacher head0.242
Teacher spread0.220 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations57
Published2005
Admission routes3
Has abstractyes

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