MétaCan
Menu
← Back to cohort
Record W3174874008 · doi:10.5194/bg-2021-166

Tidal mixing of estuarine and coastal waters in the Western English Channel controls spatial and temporal variability in seawater CO <sub>2</sub>

2021· article· en· W3174874008 on OpenAlexaff
Richard Sims, Michael Bedington, Ute Schuster, Andrew Watson, Vassilis Kitidis, Ricardo Torres, Helen S. Findlay, James Fishwick, Ian Brown, Thomas G. Bell

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsUniversity of Calgary
FundersFP7 Research infrastructuresInterregHorizon 2020Natural Environment Research CouncilSight Research UK
KeywordsEstuaryOceanographySeawaterTransectSpatial variabilitySalinityFlux (metallurgy)Environmental scienceSurface waterGeologyChannel (broadcasting)TRACERClimatology

Abstract

fetched live from OpenAlex

Abstract. Surface ocean CO2 measurements are used to compute the oceanic air–sea CO2 flux. The CO2 flux component from rivers and estuaries is uncertain. Estuarine and coastal water carbon dioxide (CO2) observations are relatively few compared to observations in the open ocean. The contribution of these regions to the global air–sea CO2 flux remains uncertain due to systematic under-sampling. Existing high-quality CO2 instrumentation predominantly utilise showerhead and percolating style equilibrators optimised for open ocean observations. The intervals between measurements made with such instrumentation make it difficult to resolve the fine-scale spatial variability of surface water CO2 at timescales relevant to the high frequency variability in estuarine and coastal environments. Here we present a novel dataset with unprecedented frequency and spatial resolution transects made at the Western Channel Observatory in the south west of the UK from June to September 2016, using a fast response seawater CO2 system. Novel observations were made along the estuarine–coastal continuum at different stages of the tide and reveal distinct spatial patterns in the surface water CO2 fugacity (fCO2) at different stages of the tidal cycle. Changes in salinity and fCO2 were closely correlated at all stages of the tidal cycle and suggest that the mixing of oceanic and riverine end members determines the variations in fCO2. The observations demonstrate the complex dynamics determining spatial and temporal patterns of salinity and fCO2 in the region. Spatial variations in observed surface salinity were used to validate the output of a regional high resolution hydrodynamic model. The model enables a novel estimate of the air–sea CO2 flux in the estuarine–coastal zone. Air–sea CO2 flux variability in the estuarine–coastal boundary region is dominated by the state of the tide because of strong CO2 outgassing from the river plume. The observations and model output demonstrate that undersampling the complex tidal and mixing processes characteristic of estuarine and coastal environment bias quantification of air-sea CO2 fluxes in coastal waters. The results provide a mechanism to support critical national and regional policy implementation by reducing uncertainty in carbon budgets.

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 score0.091
Threshold uncertainty score0.181

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.187
Teacher spread0.180 · 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

Citations2
Published2021
Admission routes1
Has abstractyes

Explore more

Same topicOceanographic and Atmospheric Processes→French-language works237,207→