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Record W6987129649

Singular value decomposition analysis of cloud fraction cover and sea ice concentration over the Arctic region, 1982-2009

2016· article· en· W6987129649 on OpenAlexaboutno aff

Bibliographic record

VenueIRIS UNIMORE (University of Modena and Reggio Emilia) · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsSea iceArcticArctic ice packSea ice concentrationArctic sea ice declineCloud coverMode (computer interface)The arcticDrift ice
DOInot available

Abstract

fetched live from OpenAlex

In this study, the coupled spatial and temporal variability between seasonal data of Cloud Fraction Cover (CFC) and Sea Ice Concentration (SIC) in the Arctic Ocean for the 1982-2009 period were investigated by using the Singular Value Decomposition (SVD) method. The spatial patterns of CFCs related to the first mode of co-variability, identify the maximum covariance, for all seasons, in the Barents Sea and in the Arctic Ocean Canada, while the SIC and 'found in the Chukchi Sea in autumn (in according to the maximum sea ice melting) and the Barents sea during both the winter and spring. CFC spatial patterns related to the first mode of co-variability, locate maximum covariance, for all seasons, in the Barents Sea and in the Canadian side of the Arctic Ocean, while for SIC is found in the Chukchi Sea during autumn (corresponding to the maximum sea ice melting) and in the Barents Sea during both the winter and spring. Finally, the correlation between the seasonal time series of expansion coefficients derived from the SVD analysis, for both CFC and SIC fields, with the seasonal time series of some relevant climate indices for the Arctic (NAO, AO, PDO and PNA). Statistically significant values for both fields were found during summer with the AO, and during autumn with the PNA.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.203
Threshold uncertainty score0.982

Codex and Gemma teacher scores by category

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

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 head0.008
GPT teacher head0.197
Teacher spread0.188 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

Explore more

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