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Record W2792436389 · doi:10.1002/2017jd027439

Trends of Cyclone Characteristics in the Arctic and Their Patterns From Different Reanalysis Data

2018· article· en· W2792436389 on OpenAlexaboutno aff
Matthias Zahn, Mirseid Akperov, Annette Rinke, Frauke Feser, И. И. Мохов

Bibliographic record

VenueJournal of Geophysical Research Atmospheres · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsnot available
FundersRussian Science FoundationDeutsche Forschungsgemeinschaft
KeywordsClimatologyArcticCyclone (programming language)The arcticArchipelagoCommon spatial patternTropical cycloneEnvironmental scienceOceanographyGeologyGeographyPhysical geography

Abstract

fetched live from OpenAlex

Abstract Cyclones in the Arctic are detected and tracked in four different reanalysis data sets from 1981 to 2010. In great detail the spatial and seasonal patterns of changes are scrutinized with regards to their frequencies, depths, and sizes. We find common spatial patterns for their occurrences, with centers of main activity over the seas in winter, and more activity over land and over the North Pole in summer. The deep cyclones are more frequent in winter, and the number of weak cyclones peaks in summer. Overall, we find a good agreement of our tracking results across the different reanalyses. Regarding the frequency changes, we find strong decreases in the Barents Sea and along the Russian coast toward the North Pole and increases over most of the central Arctic Ocean and toward the Pacific in winter. Areas of increasing and decreasing frequencies are of similar size in winter. In summer there is a longish region of increase from the Laptev Sea toward Greenland, over the Canadian archipelago, and over some smaller regions west of Novaya Zemlya and over the Russia. The larger part of the Arctic experiences a frequency decrease. All the summer changes are found statistically unrelated to the winter patterns. In addition, the frequency changes are found unrelated to changes in cyclone depth and size. There is generally good agreement across the different reanalyses in the spatial patterns of the trend sign. However, the magnitudes of changes in a particular region may strongly differ across the data.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.150
Threshold uncertainty score0.996

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.043
GPT teacher head0.300
Teacher spread0.257 · 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 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

Citations94
Published2018
Admission routes1
Has abstractyes

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