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Record W2783837650 · doi:10.1021/acs.est.7b04717

Riverine Export of Aged Carbon Driven by Flow Path Depth and Residence Time

2018· article· en· W2783837650 on OpenAlexaff
Rebecca T. Barnes, David Butman, Henry F. Wilson, Peter A. Raymond

Bibliographic record

VenueEnvironmental Science & Technology · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsAgriculture and Agri-Food Canada
FundersDivision of Environmental Biology
KeywordsDissolved organic carbonHydrology (agriculture)Environmental scienceResidence time (fluid dynamics)WeatheringArcticTotal organic carbonSTREAMSCarbon cycleCarbon fibersAridErosionEnvironmental chemistryGeologyOceanographyEcosystemChemistryEcologyGeomorphology

Abstract

fetched live from OpenAlex

The flux of terrestrial C to rivers has increased relative to preindustrial levels, a fraction of which is aged dissolved organic C (DOC). In rivers, C is stored in sediments, exported to the ocean, or (bio)chemically processed and released as CO 2 . Disturbance changes land cover and hydrology, shifting potential sources and processing of DOC. To investigate the likely sources of aged DOC, we analyzed radiocarbon ages, chemical, and spectral properties of DOC and major ions from 19 rivers draining the coterminous U.S. and Arctic. DOC optics indicated that the majority is exported as aromatic, high molecular weight, modern molecules while aged DOC tended to consist of smaller, microbial degradation products. Aged DOC exports, observed regularly in arid basins and during base flow in arctic rivers, are associated with higher proportion of mineral weathering products, suggesting deeper flows paths. These patterns also indicate potential for production of microbial byproducts as DOC ages in soil and water with longer periods of time between production and transport. Thus, changes in hydrology associated with landscape alteration (e.g., tilling or shifting climates) that can result in deeper flow paths or longer residence times will likely lead to a greater proportion of aged carbon in riverine exports.

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 score0.084
Threshold uncertainty score0.939

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 studies0.0000.002
Scholarly communication0.0000.000
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 head0.003
GPT teacher head0.165
Teacher spread0.162 · 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

Citations113
Published2018
Admission routes1
Has abstractyes

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