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Record W1978468492 · doi:10.1175/2009bams2693.1

Storm Studies in the Arctic (STAR)

2009· article· en· W1978468492 on OpenAlexaboutno aff
John Hanesiak, Ronald E. Stewart, Peter A. Taylor, G. W. K. Moore, David G. Barber, Gordon McBean, W. Strapp, Mengistu Wolde, Ron Goodson, E. Hudson, David Hudak, J. W. Scott, Chaojie Liu, Justin Gilligan, Sumita Biswas, Danielle Desjardins, Robyn Dyck, Shannon Elizabeth Fargey, Robert D. Field, Gabrielle Gascon, Mark Gordon, Heather Greene, Carling C. Hay, William Henson, Klaus P. Hochheim, Alex Laplante, Rebekah Martin, Marna Albarran Melzer, Shunli Zhang

Bibliographic record

VenueBulletin of the American Meteorological Society · 2009
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsArcticStormEnvironmental scienceTerrainConvective storm detectionMeteorologyMesoscale meteorologyClimatologyWinter stormPrecipitationGeologyGeographyOceanography

Abstract

fetched live from OpenAlex

The Storm Studies in the Arctic (STAR) network (2007–2010) conducted a major meteorological field project from 10 October–30 November 2007 and in February 2008, focused on southern Baffin Island, Nunavut, Canada—a region that experiences intense autumn and winter storms. The STAR research program is concerned with the documentation, better understanding, and improved prediction of meteorological and related hazards in the Arctic, including their modification by local topography and land–sea ice–ocean transitions, and their effect on local communities. To optimize the applicability of STAR network science, we are also communicating with the user community (northern communities and government sectors). STAR has obtained a variety of surface-based and unique research aircraft field measurements, high-resolution modeling products, and remote sensing measurements (including Cloudsat) as part of its science strategy and has the first arctic Cloudsat validation dataset. In total, 14 research flights were flown between 5 and 30 November 2007, with eight coinciding with Cloudsat passes. The aircraft was outfitted with many instruments that measure cloud microphysical parameters and three unique Doppler-polarized airborne radars operating in Ka, X and W bands. The project area, instrumentation platforms, real-time forecasts, storm cases, and results thus far are discussed in this article. A number of synoptic and mesoscale features were sampled—such as fronts, upslope/terrain-enhanced precipitation, convective precipitation, and boundary layer clouds/precipitation—as well as targeted Cloudsat missions. One significant and unique event included a research flight into an intense high-latitude storm leftover from Hurricane Noel—an intense tropical and extratropical disturbance that caused many fatalities in the tropics and extensive damage on the eastern North American seaboard. These synoptic and mesoscale features and high-latitude storms will be studied in detail over the next several years. It is anticipated that scientific progress in better understanding the nature of these arctic storms and their hazards will lead to improved conceptual models and improved prediction of such events.

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)0.003
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.205
Threshold uncertainty score0.408

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.025
GPT teacher head0.256
Teacher spread0.231 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations27
Published2009
Admission routes1
Has abstractyes

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