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Record W2967723433 · doi:10.2475/06.2019.02

Carbon dioxide emissions by rock organic carbon oxidation and the net geochemical carbon budget of the Mackenzie River Basin

2019· article· en· W2967723433 on OpenAlexfundaboutno aff
Kate Horan, Robert Hilton, Mathieu Dellinger, Edward T. Tipper, Valier Galy, Damien Calmels, David Selby, Jérôme Gaillardet, Chris J. Ottley, Daniel R. Parsons, Kevin W. Burton

Bibliographic record

VenueAmerican Journal of Science · 2019
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsnot available
FundersNatural Environment Research CouncilBritish Society for GeomorphologyCentre National de la Recherche ScientifiqueSight Research UKAurora Research InstituteNational Science Foundation
KeywordsWeatheringTotal organic carbonCarbon dioxideCarbonateEnvironmental chemistryGeologySedimentTributaryDissolved organic carbonCarbon fibersDrainage basinEnvironmental scienceGeochemistryChemistryGeomorphology

Abstract

fetched live from OpenAlex

The exposure of organic carbon in rocks to oxidative weathering can release carbon dioxide (CO~2~) to the atmosphere and consume atmospheric oxygen. Alongside volcanism, metamorphism, and the weathering of carbonate minerals by sulfuric acid, this is a major source of atmospheric CO~2~ over million year timescales. The balance between CO~2~ release and CO~2~ drawdown by silicate weathering and organic carbon burial sets the net geochemical carbon budget during weathering and erosion. However, the rates of rock-derived organic carbon (petrogenic organic carbon, OC~petro~) oxidation remain poorly constrained. Here, we use rhenium as a proxy to trace and quantify CO~2~ release by OC~petro~ oxidation in the Mackenzie River Basin, Canada, where the other carbon fluxes have been well constrained previously. River water and sediment samples were collected between 2009 and 2013 at gauging stations along the Mackenzie River and its main tributaries (Liard, Peel and Arctic Red). To assess rhenium inputs from silicate, sulfide and OC~petro~ mineral phases we normalize dissolved rhenium concentrations, \[Re\]~diss~, to sodium and sulfate ion concentrations. This approach suggests that \>85 percent of \[Re\]~diss~ is derived from OC~petro~ in the main river channels. \[Re\]~diss~ and water discharge measurements are used to quantify dissolved Re yields. River sediments provide a measure of the Re to OC~petro~ ratio of materials undergoing weathering in the basin, and agree well with published rock samples. Dissolved Re yields are combined with river sediment \[Re\]/\[OC~petro~\] ratios to estimate the CO~2~ emissions by OC~petro~ weathering. These are 0.45 ^+0.19^/~−0.11~ metric tonnes of carbon, tC km^−2^ yr^−1^for the Mackenzie River at Tsiigehtchic (3.8 ^+1.5^/~−0.9~ × 10^4^ moles km^−2^ yr^−1^), and 0.94 ^+0.41^/~−0.26~ tC km^−2^ yr^−1^, 0.78 ^+0.35^/~−0.21~ tC km^−2^ yr^−1^ and 1.01 ^+0.42^/~−0.25~ tC km^−2^ yr^−1^ for the Peel, Arctic Red and Liard catchments, respectively. When considered alongside published silicate and carbonate weathering rates and the sedimentary burial of biospheric organic carbon, these data suggest that the upper part of the Mackenzie River Basin presently acts as an atmospheric CO~2~ sink of ∼1 tC km^−2^ yr^−1^ (∼8 × 10^4^ moles km^−2^ yr^−1^) as a result of the carbon transfers by weathering and erosion. During the Last Glacial Maximum, it is possible that the net geochemical carbon balance may have been very different: potential increases in CO~2~ emissions from oxidative weathering of OC~petro~ and carbonate minerals, coupled with reduced biospheric carbon burial, may have tipped the balance to a net source of CO~2~.

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)0.000
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 score0.064
Threshold uncertainty score0.129

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.193
Teacher spread0.190 · 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

Citations83
Published2019
Admission routes2
Has abstractyes

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