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Record W4383876264 · doi:10.5194/essd-15-2879-2023

GRiMeDB: the Global River Methane Database of concentrations and fluxes

2023· article· en· W4383876264 on OpenAlex
Emily H. Stanley, Luke C. Loken, Nora J. Casson, Samantha K. Oliver, Ryan A. Sponseller, Marcus B. Wallin, Liwei Zhang, Gerard Rocher‐Ros

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEarth system science data · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsUniversity of Winnipeg
FundersNorges ForskningsrådVetenskapsrådetSvenska Forskningsrådet FormasUmeå UniversitetNational Science Foundation
KeywordsEnvironmental scienceFluvialMethaneEcosystemSTREAMSAtmosphere (unit)Hydrology (agriculture)Flux (metallurgy)River ecosystemAquatic ecosystemAtmospheric sciencesDatabaseEnvironmental chemistryEcologyGeologyMeteorologyChemistryComputer scienceGeography

Abstract

fetched live from OpenAlex

Abstract. Despite their small spatial extent, fluvial ecosystems play a significant role in processing and transporting carbon in aquatic networks, which results in substantial emission of methane (CH4) into the atmosphere. For this reason, considerable effort has been put into identifying patterns and drivers of CH4 concentrations in streams and rivers and estimating fluxes to the atmosphere across broad spatial scales. However, progress toward these ends has been slow because of pronounced spatial and temporal variability of lotic CH4 concentrations and fluxes and by limited data availability across diverse habitats and physicochemical conditions. To address these challenges, we present a comprehensive database of CH4 concentrations and fluxes for fluvial ecosystems along with broadly relevant and concurrent physical and chemical data. The Global River Methane Database (GriMeDB; https://doi.org/10.6073/pasta/f48cdb77282598052349e969920356ef, Stanley et al., 2023) includes 24 024 records of CH4 concentration and 8205 flux measurements from 5029 unique sites derived from publications, reports, data repositories, unpublished data sets, and other outlets that became available between 1973 and 2021. Flux observations are reported as diffusive, ebullitive, and total CH4 fluxes, and GriMeDB also includes 17 655 and 8409 concurrent measurements of concentrations and 4444 and 1521 fluxes for carbon dioxide (CO2) and nitrous oxide (N2O), respectively. Most observations are date-specific (i.e., not site averages), and many are supported by data for 1 or more of 12 physicochemical variables and 6 site variables. Site variables include codes to characterize marginal channel types (e.g., springs, ditches) and/or the presence of human disturbance (e.g., point source inputs, upstream dams). Overall, observations in GRiMeDB encompass the broad range of the climatic, biological, and physical conditions that occur among world river basins, although some geographic gaps remain (arid regions, tropical regions, high-latitude and high-altitude systems). The global median CH4 concentration (0.20 µmol L−1) and diffusive flux (0.44 mmolm-2d-1) in GRiMeDB are lower than estimates from prior site-averaged compilations, although ranges (0 to 456 µmol L−1 and −136 to 4057 mmolm-2d-1) and standard deviations (10.69 and 86.4) are greater for this larger and more temporally resolved database. Available flux data are dominated by diffusive measurements despite the recognized importance of ebullitive and plant-mediated CH4 fluxes. Nonetheless, GriMeDB provides a comprehensive and cohesive resource for examining relationships between CH4 and environmental drivers, estimating the contribution of fluvial ecosystems to CH4 emissions, and contextualizing site-based investigations.

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.

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)0.001
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 score0.267
Threshold uncertainty score0.853

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.001
Open science0.0010.002
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 head0.022
GPT teacher head0.255
Teacher spread0.233 · 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