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Record W4384562770 · doi:10.1029/2022ef003242

Marine Heatwaves Contribute More to Changing Air‐Water Exchange of Semi‐Volatile Organic Compounds Than Mean Sea Surface Temperature Rise

2023· article· en· W4384562770 on OpenAlexaff
Qinya Fan, Yulong Yao, Qihang Liao, Hongyu Chen, Xinqing Zou, Guanghe Fu, Ziyue Feng, Yongcheng Ding, Feng Yuan

Bibliographic record

VenueEarth s Future · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsMinistry of Education and Child Care
FundersFundamental Research Funds for the Central UniversitiesGovernment of Jiangsu Province
KeywordsEnvironmental scienceBiogeochemical cycleFlux (metallurgy)Global warmingAtmospheric sciencesSurface air temperatureEnvironmental chemistryOceanographyChemistryClimate changeGeology

Abstract

fetched live from OpenAlex

Abstract A consequence of global warming, marine heatwaves (MHWs), have destructive effects on local marine ecology. However, few studies have examined their influence on the biogeochemical carbon cycle. Here we show, global warming has changed the air‐water exchange of semi‐volatile organic compounds (SVOCs), and MHWs contribute more to changing air‐water exchange of SVOCs than increases in mean sea surface temperature (SST). Higher temperatures increased the flux of SVOCs exchange from water to air and suppressed the exchange from air to water. As a result, the MHWs lead to a 13.27% decrease for the daily flux of single SVOC and 0.05% decrease for annual flux of total SVOCs. In contrast, the raising of mean SST result of 0.14% decrease for the daily flux of single SVOC, and 0.01% decrease for annual flux of total SVOCs. Moreover, we observed inversion in the direction of SVOCs air‐water exchange during some MHWs.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.194
Threshold uncertainty score0.996

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.006
GPT teacher head0.189
Teacher spread0.183 · 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.

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

Citations5
Published2023
Admission routes1
Has abstractyes

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