MétaCan
Menu
Back to cohort
Record W2790124446 · doi:10.1002/2017gl076907

Multi‐Instrument Observations of Prolonged Stratified Wind Layers at Iqaluit, Nunavut

2018· article· en· W2790124446 on OpenAlexafffundabout
Zen Mariani, Armin Dehghan, Gabrielle Gascon, Paul Joe, David Hudak, K. B. Strawbridge, Julien Corriveau

Bibliographic record

VenueGeophysical Research Letters · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsEnvironment and Climate Change Canada
FundersEnvironment and Climate Change Canada
KeywordsRadiosondeLidarStratification (seeds)RadarWind profilerEnvironmental scienceDoppler radarGeologyWind speedMeteorologyWind shearPrevailing windsWind directionAtmospheric sciencesRemote sensingOceanographyGeography

Abstract

fetched live from OpenAlex

Abstract Data collected between October 2015 and May 2016 at Environment and Climate Change Canada's Iqaluit research site (64°N, 69°W) have revealed a high frequency (40% of all days for which observations were available) of stratified wind layer events that occur from near the surface up to about 7.2 km above sea level. These stratified wind layers are clearly visible as wind shifts (90 to 180°) with height in range‐height indicator scans from the Doppler lidar and Ka‐band radar and in wind direction profiles from the Doppler lidar and radiosonde. During these events, the vertical structure of the flow appears to be a stack of 4 to 10 layers ranging in vertical width from 0.1 to 4.4 km. The stratification events that were observed occurred predominantly (81%) during light precipitation and lasted up to 27.5 h. The integrated measurement platforms at Iqaluit permitted continuous observations of the evolution of stratification events in different meteorological conditions.

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 score0.133
Threshold uncertainty score0.267

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
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 head0.050
GPT teacher head0.290
Teacher spread0.240 · 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
Published2018
Admission routes3
Has abstractyes

Explore more

Same venueGeophysical Research LettersSame topicAtmospheric and Environmental Gas DynamicsFrench-language works237,207