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Record W4365814902 · doi:10.1038/s41467-023-37861-7

Evaluation of the methane paradox in four adjacent pre-alpine lakes across a trophic gradient

2023· article· en· W4365814902 on OpenAlexaff
César Ordóñez, Tonya DelSontro, Timon Langenegger, Daphne Donis, Ena Suarez, Daniel F. McGinnis

Bibliographic record

VenueNature Communications · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsUniversity of Waterloo
FundersHorizon 2020 Framework ProgrammeEuropean CommissionSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsMethaneEnvironmental scienceAtmospheric methaneTrophic levelMethane emissionsAtmosphere (unit)Anoxic watersEnvironmental chemistryAtmospheric sciencesEcologyChemistryGeologyBiologyPhysicsMeteorology

Abstract

fetched live from OpenAlex

Contrasting the paradigm that methane is only produced in anoxic conditions, recent discoveries show that oxic methane production (OMP, aka the methane paradox) occurs in oxygenated surface waters worldwide. OMP drivers and their contribution to global methane emissions, however, are not well constrained. In four adjacent pre-alpine lakes, we determine the net methane production rates in oxic surface waters using two mass balance approaches, accounting for methane sources and sinks. We find that OMP occurs in three out of four studied lakes, often as the dominant source of diffusive methane emissions. Correlations of net methane production versus chlorophyll-a, Secchi and surface mixed layer depths suggest a link with photosynthesis and provides an empirical upscaling approach. As OMP is a methane source in direct contact with the atmosphere, a better understanding of its extent and drivers is necessary to constrain the atmospheric methane contribution by inland waters.

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 score0.029
Threshold uncertainty score0.058

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.032
GPT teacher head0.313
Teacher spread0.282 · 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

Citations47
Published2023
Admission routes1
Has abstractyes

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