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Record W4393766168 · doi:10.5281/zenodo.7378599

Composite tracks of Mediterranean cyclones (1979-2020)

2022· dataset· en· W4393766168 on OpenAlexaff
Emmanouil, Leonardo Leonardo, Lisa, Stavros,  Benjamin, Helena, L. Suzanne, Alexia, John K. John, Piero, Florian, Claudia Cláudia, Platón, Maria Angeles, Federico Federico, K. Matthew D., M D Marco, Malcolm, Hadas, Dor, Enrico Enrico, Michael Michael, Baruch

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2022
Typedataset
Languageen
FieldEarth and Planetary Sciences
TopicTropical and Extratropical Cyclones Research
Canadian institutionsCollège Lionel Groulx
Fundersnot available
KeywordsMediterranean climateComposite numberCyclone (programming language)MeteorologyGeographyEnvironmental scienceGeologyArchaeologyEngineeringMaterials scienceComposite material

Abstract

fetched live from OpenAlex

The composite tracks of Mediterranean cyclones have been produced by combining an ensembles of 10 independent track datasets, applied to ERA5 reanalysis for the period 1979-2020. Composite tracks describe more intense and longer-lasting cyclones with more distinguished early, mature and decay stages than cyclones coming from individual tracking methods. Composite tracks are ranked according to their confidence level, i.e. the number of individual cyclone tracking methods that tracked the same cyclone. For each confidence level, we provide a separate file that includes a matrix of eight columns and a number of rows that varies among the datasets. Each row corresponds to a single track point, while the eight columns provide the following information: - Column 1: A cumulatively increasing index that functions as an identifier of unique cyclone tracks. For instance, all information about the track of cyclone #456 are found in all rows starting with the number 456. - Column 2: Longitude of track points - Column 3: Latitude of track points. It is important to note that geographical coordinates are produced using Step 2 of our method and thus may not match the exact location of grid points of ERA5. - Column 4: Year of occurrence - Column 5: Month of occurrence - Column 6: Day of occurrence - Column 7: Hour of occurrence - Column 8: Lowest MSLP value (in hPa) within a 2.5 degrees radius from the geographical coordinates in columns 2 and 3. These values are only meant to function as an approximate reference of intensity.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient 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 score0.603
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.6090.006

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 head0.032
GPT teacher head0.243
Teacher spread0.211 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2022
Admission routes1
Has abstractyes

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Same venueZenodo (CERN European Organization for Nuclear Research)Same topicTropical and Extratropical Cyclones ResearchFrench-language works237,207