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Record W3136168887 · doi:10.1038/s43247-021-00192-w

Carbon dioxide fluxes increase from day to night across European streams

2021· article· en· W3136168887 on OpenAlexaff
Katrin Attermeyer, Joan Pere Casas‐Ruiz, Thomas Fuß, Ada Pastor, Sophie Cauvy‐Fraunié, Danny Sheath, Anna Nydahl, A. Doretto, Ana Paula Portela, Brian C. Doyle, Nikolay Simov, Catherine Gutmann Roberts, Georg H. Niedrist, Xisca Timoner, Vesela Evtimova, Laura Barral-Fraga, Tea Bašić, Joachim Audet, Anne Deininger, Georgina Busst, Stefano Fenoglio, Núria Catalán, Elvira de Eyto, Francesca Pilotto, Jordi‐René Mor, J.L.F. Monteiro, David Fletcher, Christian Noß, Miriam Colls, Magdalena Nagler, Liu Liu, Clara Romero González‐Quijano, Ferran Romero, Nina Pansch, José L. J. Ledesma, Josephine Pegg, Marcus Klaus, Anna Freixa, Sonia Herrero Ortega, Clara Mendoza‐Lera, Adam Bednařík, Jérémy Fonvielle, Peter Gilbert, Lyubomir Kenderov, Martin Rulı́k, Pascal Bodmer

Bibliographic record

VenueCommunications Earth & Environment · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsUniversité du Québec à Montréal
FundersFundação para a Ciência e a TecnologiaLeibniz-GemeinschaftLeibniz-Institut für Gewässerökologie und BinnenfischereiEuropean CommissionOffice National de l’Eau et des Milieux AquatiquesUniversität Wien
KeywordsCarbon dioxideSTREAMSDiel vertical migrationEnvironmental scienceFluvialCarbon dioxide in Earth's atmosphereFlux (metallurgy)Atmospheric sciencesOceanographyEnvironmental chemistryHydrology (agriculture)ChemistryEcologyGeologyBiology

Abstract

fetched live from OpenAlex

Abstract Globally, inland waters emit over 2 Pg of carbon per year as carbon dioxide, of which the majority originates from streams and rivers. Despite the global significance of fluvial carbon dioxide emissions, little is known about their diel dynamics. Here we present a large-scale assessment of day- and night-time carbon dioxide fluxes at the water-air interface across 34 European streams. We directly measured fluxes four times between October 2016 and July 2017 using drifting chambers. Median fluxes are 1.4 and 2.1 mmol m −2 h −1 at midday and midnight, respectively, with night fluxes exceeding those during the day by 39%. We attribute diel carbon dioxide flux variability mainly to changes in the water partial pressure of carbon dioxide. However, no consistent drivers could be identified across sites. Our findings highlight widespread day-night changes in fluvial carbon dioxide fluxes and suggest that the time of day greatly influences measured carbon dioxide fluxes across European streams.

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)0.001
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 score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.014
GPT teacher head0.205
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

Citations69
Published2021
Admission routes1
Has abstractyes

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