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Record W6944064699 · doi:10.17632/mtwfvcvy5z

Cold Air Outbreaks

2020· dataset· en· W6944064699 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueMendeley Data · 2020
Typedataset
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsPercentileLongitudeExtreme ColdAir temperatureClimate changeData setStandard deviationTemporal resolution

Abstract

fetched live from OpenAlex

Overview: This study uses a set of criteria to examine cold air outbreaks (CAOs) across the globe from 1979 – 2018 and to determine how CAOs have changed over the last 40 years. We found CAOs occur most frequently in the Northern Hemisphere, with as many as 8 CAO days per year in North America and Eurasia. CAOs were found to have decreased in size, intensity, frequency, and duration across much of the globe, with the largest decreases in Alaska, Canada, and the North Atlantic, while an increase in CAOs was observed in Eastern Europe, Central Eurasia, and the Southern Ocean. Early and late winter CAOs have also become much less frequent in most regions. Data Used: Two-meter temperature (T2m) data was acquired from the NCEP/NCAR (NNR) climate reanalysis dataset (National Center for Environmental Prediction (NCEP) and National Center for Atmospheric Research (NCAR) and the recently released ERA5 reanalysis data set from the European Center for Medium-Range Weather Forecasts (ECMWF). ERA5 T2m was acquired at a 1 degree spatial resolution on an hourly timescale and converted to daily mean T2m while NNR daily mean T2m was acquired at a T62 gaussian grid (192 longitude and 94 latitude) spatial resolution from 1979 - 2018. CAO Methods: Three criteria for a CAO were designed to capture the most extreme CAOs while being flexible enough to capture the entire evolution of the event. 1.) Magnitude: The magnitude criterion requires the daily mean temperature to be at or below the 2.5th percentile threshold of deseasonalized 2-meter temperature (T2m). The daily mean T2m must also be at or below 20 degrees Celsius with a departure from the climatological mean of at least -2 degrees Celsius. 2.) Spatial Extent: The daily spatial extent, which is a summation of all contiguous grid points that meet the magnitude criteria, must be at least 1,000,000 km2. 3.) Duration: The duration criterion requires the magnitude criterion for the entire CAO be met for at least five consecutive days and begins on the first day in which the spatial extent criterion is met and ends on the last day the spatial extent criterion is met. How to use and interpret data: There are 3 files: 1.) and excel file of all CAOs for both the NNR and ERA5 (separate tabs). Because the ERA5 data is the primary data set used in this study it has two additional columns of data, one for the region of the CAO and one for the hemisphere of the CAO. 2.) A .mat file (MATLAB) of all the ERA5 CAO data. The column headers are as follows: [1. daily data for each CAO event, 2. onset date, 3. duration, 4. Mean z-score 5. mean z-score per gridpoint, 6. total duration per gridpoint 7. daily z-score per gridpoint 8. temperature anomaly each day, 9. Region 10. hemisphere] 3.) A similar .mat file, but for the NNR CAOs. Differences: columns 4 and 5 and 11 in the NNR file are not in the ERA5 file (shift headers). These were used in calculations but omitted from ERA5 file for size restraints.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Open science, Insufficient payload (model declined to judge)
Consensus categoriesOpen science, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.263
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0090.008
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.265

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 head0.101
GPT teacher head0.324
Teacher spread0.222 · 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

Quick stats

Citations1
Published2020
Admission routes1
Has abstractyes

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