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Record W2809870507 · doi:10.1021/acs.est.8b01051

Increasing Alkalinity Export from Large Russian Arctic Rivers

2018· article· en· W2809870507 on OpenAlexaff
Travis W. Drake, Suzanne E. Tank, Alexander V. Zhulidov, R. M. Holmes, Tatiana Gurtovaya, Robert G. M. Spencer

Bibliographic record

VenueEnvironmental Science & Technology · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsUniversity of Alberta
FundersOffice of Polar Programs
KeywordsAlkalinityPermafrostEnvironmental scienceArcticWeatheringEcosystemClimate changeSink (geography)PrecipitationHydrology (agriculture)Carbon sinkGlobal warmingVegetation (pathology)CarbonateGroundwaterOceanographyGeologyEcologyGeographyChemistryGeochemistry

Abstract

fetched live from OpenAlex

Riverine carbonate alkalinity (HCO3– and CO32–) sourced from chemical weathering represents a significant sink for atmospheric CO2. Alkalinity flux from Arctic rivers is partly determined by precipitation, permafrost extent, groundwater flow paths, and surface vegetation, all of which are changing under a warming climate. Here we show that over the past three and half decades, the export of alkalinity from the Yenisei and Ob’ Rivers increased from 225 to 642 Geq yr–1 (+185%) and from 201 to 470 Geq yr–1 (+134%); an average rate of 11.90 and 7.28 Geq yr–1, respectively. These increases may have resulted from a suite of changes related to climate change and anthropogenic activity, including higher temperatures, increased precipitation, permafrost thaw, changes to hydrologic flow paths, shifts in vegetation, and decreased acid deposition. Regardless of the direct causes, these trends have broad implications for the rate of carbon sequestration on land and delivery of buffering capacity to freshwater ecosystems and the Arctic Ocean.

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 categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0230.002

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 head0.015
GPT teacher head0.223
Teacher spread0.208 · 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; both teacher heads agree on what is shown here.

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

Citations127
Published2018
Admission routes1
Has abstractyes

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