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Study of dissolved oxygen responses to tropical cyclones in the Bay of Bengal based on Argo and satellite observations

2018· article· en· W2906676101 on OpenAlexaff
Huabing Xu, Danling Tang, Jinyu Sheng, Yupeng Liu, Yi Sui

Bibliographic record

VenueThe Science of The Total Environment · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTropical and Extratropical Cyclones Research
Canadian institutionsDalhousie University
FundersNational Natural Science Foundation of China
KeywordsUpwellingArgoDownwellingOceanographyBayMesoscale meteorologyTropical cycloneGeologyEnvironmental scienceStormEkman transportClimatology

Abstract

fetched live from OpenAlex

Effects of tropical cyclones (TCs) on dissolved oxygen (DO) in subsurface waters (20-200 m) over the Oxygen Minimum Zones (OMZs) in the Bay of Bengal (BoB) are examined based on Argo and satellite data. Five TCs (Hudhud, Five, Vardah, Maarutha and Mora) during 2013-2018 are considered. Analyses reveal three types of DO temporal variability caused by the storm-induced mixing and upwelling. The first type features temporal DO increases in subsurface waters (37-70 m) caused mainly by intense vertical mixing and downwelling. The second type features DO reductions in subsurface waters after the storms attributed to storm-induced upwelling. The third type features temporal DO increases at depths between 40 and 79 m and decreases at depths between 80 and 150 m due to the combined effect of strong vertical mixing and upwelling. These three types of DO responses can occur in different areas, depending on TC intensity, translational speed and Ekman pumping. The temporal DO variability is also influenced by the shallow oxycline (58.3 ± 16.7 m), mesoscale eddies and biochemical processes. Due to TC intensification, a pre-existing oceanic cyclonic eddy produced a large upwelling and induced a long time of DO decrease in the subsurface layer. This study suggests three different types of DO responses along the TC track in the OMZ, which is useful to evaluate the influence of TCs on the OMZ.

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 score0.025
Threshold uncertainty score0.546

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.037
GPT teacher head0.253
Teacher spread0.216 · 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

Citations26
Published2018
Admission routes1
Has abstractyes

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