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Record W4281680503 · doi:10.1029/2021gb007161

Hydrologic Export of Soil Organic Carbon: Continental Variation and Implications

2022· article· en· W4281680503 on OpenAlexfundno aff
Oleksandra Hararuk, Stuart E. Jones, Christopher T. Solomon

Bibliographic record

VenueGlobal Biogeochemical Cycles · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil and Water Nutrient Dynamics
Canadian institutionsnot available
FundersFonds de recherche du Québec – Nature et technologiesNational Science Foundation
KeywordsEnvironmental scienceAridSoil carbonSink (geography)Carbon cycleHydrology (agriculture)Total organic carbonSoil waterSoil organic matterCarbon sinkDrainage basinSoil scienceEnvironmental chemistryClimate changeEcosystemEcologyGeologyOceanographyGeographyChemistry

Abstract

fetched live from OpenAlex

Abstract Soil is the largest terrestrial carbon (C) reservoir and a large potential source or sink of atmospheric CO ₂ . Soil C models have usually focused on refining representations of microbe‐mediated C turnover, whereas lateral hydrologic C fluxes have largely been ignored at regional and global scales. Here, we provide large‐scale estimates of hydrologic export of soil organic carbon (SOC) and its effects on bulk soil C turnover rates. Hydrologic export of SOC ranged from nearly 0 to 12 g C m −2 yr −1 amongst catchments across the conterminous United States, and total export across this region was 14 (95% CI 4‐41) Tg C/yr. The proportion of soil C turnover attributed to hydrologic export ranged from <1% to 20%, and averaged 0.97% (weighted by catchment area; 95% CI 0.3%–2.6%), with the lowest values in arid catchments. Ignoring hydrologic export in C cycle models might lead to overestimation of SOC stocks by 0.3–2.6 Pg C for the conterminous United States. High uncertainty in hydrologic C export fluxes and potentially substantial effects on soil C turnover illustrate the need for research aimed at improving our mechanistic understanding of the processes regulating hydrologic C export.

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 score0.072
Threshold uncertainty score0.324

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.006
GPT teacher head0.202
Teacher spread0.196 · 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

Citations8
Published2022
Admission routes1
Has abstractyes

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