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Record W2931157720 · doi:10.1080/16000870.2019.1591856

Decadal variance of summer near-surface temperature maximum in Canada Basin of Arctic Ocean

2019· article· en· W2931157720 on OpenAlexaboutno aff
Long Lin, Hailun He

Bibliographic record

VenueTellus A Dynamic Meteorology and Oceanography · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsnot available
FundersNational Key Research and Development Program of ChinaChina Academy of Engineering PhysicsNational Natural Science Foundation of China
KeywordsArcticStructural basinCanada BasinClimatologyPeriod (music)Sea iceEnvironmental scienceSea surface temperatureOceanographyGeologyAtmospheric sciences

Abstract

fetched live from OpenAlex

In the summer Arctic, bump-like vertical temperature profiles of the upper layer in the Canada Basin suggest a near-surface temperature maximum (NSTM) beneath the mixed layer. This paper concentrates on describing the decadal variance of these NSTMs. Essentially, the temporal evolution of the summer NSTM revealed three decadal phases. The first period is before 2003, when the summer NSTM could rarely be observed except around the marginal of the Canada Basin. The second period is between 2003 and 2015, when the summer NSTM nearly occurred over the whole basin as accelerated decline of summer sea ice. The third period is from 2016 to 2017, when the summer NSTM almost disappeared due to prevailing warm surface water. Furthermore, for the background behind the decadal variance of summer NSTM, linear trends of the September minimum sea ice extent and surface water heat content in the Canada Basin from 2003 to 2017 were –2.75 ± 1.08 × 104 km<sup>2</sup>yr<sup>−1</sup> and 2.29 ± 1.36 MJ m<sup>−2</sup>yr<sup>−1</sup>, respectively. According to a previous theory, if we assume that the trend of the summer surface water heat content was only contributed by NSTM, it would cause a decrease in sea ice thickness of approximately 13 cm. The analysis partially explains the reason for sea ice decline in recent years.

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 score0.132
Threshold uncertainty score0.757

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.004
GPT teacher head0.179
Teacher spread0.175 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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