MétaCan
Menu
← Back to cohort

The Complex Role of Storms in Modulating Air-Sea CO2 Fluxes in the sub-Antarctic Southern Ocean.

2023· preprint· en· W4390271598 on OpenAlexaff
Tesha Toolsee, Sarah Nicholson, Pedro M. S. Monteiro

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsCanadian Society of Intestinal Research
FundersCouncil of Scientific and Industrial Research, IndiaNational Research FoundationEuropean Commission
KeywordsStormEnvironmental scienceEntrainment (biomusicology)Stratification (seeds)OceanographyClimatologyAtmospheric sciencesGeology

Abstract

fetched live from OpenAlex

The intra-seasonal CO2 flux (FCO2) variability across the Southern Ocean is poorly understood due to sparse observations at the required temporal and spatial scales. Twinned Waveglider-Seaglider experiments were used to investigate how storms influence FCO2 through both the gas transfer velocity (kw) and the air-sea gradient in partial pressure of CO2 (ΔpCO2) in the sub-Antarctic zone. Winter-spring storms caused ΔpCO2 to weaken (by 15-55 μatm) due to mixing/entrainment and weaker stratification. This response in ΔpCO2 was in phase with kw resulting in a counteractive weakening in FCO2 (by 6.6 - 26.5% per storm), despite the wind-driven increase in kw. Stronger stratification during summer explained the weaker sensitivity of ΔpCO2 to storms, instead its thermal drivers dominated the ΔpCO2 variability. These results highlight the importance of observing synoptic-scale variability in ΔpCO2, the absence of which may propagate significant biases to the mean annual FCO2 estimates from large-scale observing programmes and reconstructions.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.010
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

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 head0.027
GPT teacher head0.218
Teacher spread0.191 · 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 source (direct Gemma or distilled Codex), 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
Published2023
Admission routes1
Has abstractyes

Explore more

Same topicMarine and coastal ecosystems→French-language works237,207→