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Record W3023392357 · doi:10.1038/s41467-020-15929-y

Global CO2 emissions from dry inland waters share common drivers across ecosystems

2020· article· en· W3023392357 on OpenAlexfundno aff
Philipp S. Keller, Núria Catalán, Daniel von Schiller, Hans‐Peter Grossart, Matthias Koschorreck, Biel Obrador, Marieke A. Frassl, Nusret Karakaya, Nathan Barros, Julia Howitt, Clara Mendoza‐Lera, Ada Pastor, Giovanna Flaim, Ralf Aben, Tenna Riis, María Isabel Arce, Gabriela Onandía, José R. Paranaíba, Annika Linkhorst, Rubén del Campo, André Megali Amado, Sophie Cauvy‐Fraunié, Soren Brothers, Jason Condon, Raquel Mendonça, Florian Reverey, Eva‐Ingrid Rõõm, Thibault Datry, Fábio Roland, Alo Laas, Ulrike Obertegger, Jin Ha Park, Haijun Wang, Sarian Kosten, Rosa Gómez, Claudia Feijoó, Arturo Elosegi, María del Mar Sánchez‐Montoya, C. Max Finlayson, Marco Melita, Ernandes Sobreira Oliveira, Claumir César Muniz, Lluís Gómez‐Gener, Catherine Leigh, Q. Zhang, Rafael Marcé

Bibliographic record

VenueNature Communications · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsnot available
FundersAgencia Estatal de InvestigaciónErasmus+Banco Bilbao Vizcaya ArgentariaAgence française pour la biodiversitéOffice National de l’Eau et des Milieux AquatiquesConselho Nacional de Desenvolvimento Científico e TecnológicoUniversidad de MurciaNederlandse Organisatie voor Wetenschappelijk OnderzoekNational Research Foundation of KoreaEusko JaurlaritzaCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorGlobal Lake Ecological Observatory NetworkDeutscher Akademischer AustauschdienstBundesministerium für Bildung und ForschungEuropean Regional Development FundCarlsbergfondetEuropean CommissionDeutsche ForschungsgemeinschaftEesti TeadusagentuurNational Research FoundationFundación SénecaMinisterio de Ciencia, Innovación y UniversidadesFundación Ramón ArecesFundación BBVA
KeywordsEcosystemEnvironmental scienceOceanographyEnvironmental protectionEcologyBiologyGeology

Abstract

fetched live from OpenAlex

Abstract Many inland waters exhibit complete or partial desiccation, or have vanished due to global change, exposing sediments to the atmosphere. Yet, data on carbon dioxide (CO 2 ) emissions from these sediments are too scarce to upscale emissions for global estimates or to understand their fundamental drivers. Here, we present the results of a global survey covering 196 dry inland waters across diverse ecosystem types and climate zones. We show that their CO 2 emissions share fundamental drivers and constitute a substantial fraction of the carbon cycled by inland waters. CO 2 emissions were consistent across ecosystem types and climate zones, with local characteristics explaining much of the variability. Accounting for such emissions increases global estimates of carbon emissions from inland waters by 6% (~0.12 Pg C y −1 ). Our results indicate that emissions from dry inland waters represent a significant and likely increasing component of the inland waters carbon cycle.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.468
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.001
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 head0.020
GPT teacher head0.259
Teacher spread0.240 · 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

Citations176
Published2020
Admission routes1
Has abstractyes

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