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Record W4387087788 · doi:10.1130/g51571.1

“Wrecking the rocks”: Continental weathering by groundwater

2023· article· en· W4387087788 on OpenAlexafffund
Warren W. Wood, Ward E. Sanford, John A. Cherry, Warren T. Wood

Bibliographic record

VenueGeology · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGroundwater and Isotope Geochemistry
Canadian institutionsUniversity of Guelph
FundersOffice of Naval ResearchCollege of Engineering, Michigan State UniversityU.S. Geological SurveyUniversity of WaterlooMichigan State University
KeywordsWeatheringGeologyGroundwaterFlux (metallurgy)Hydrology (agriculture)Groundwater rechargeDenudationGeochemistryContinental shelfOceanographyEarth scienceGeomorphologyAquiferPaleontology

Abstract

fetched live from OpenAlex

Abstract Groundwater solute mass flux discharged from the continents to the oceans is between 56% and 63% of particulate sediment transport mass flux. Herein we utilized newly developed continental geospatial groundwater concentration estimates that were multiplied by groundwater volumetric recharge flux to provide a continental-scale discharge mass flux to the oceans of 7.3 Pg DS/yr (petagrams dissolved solutes per year). This mass flux was evaluated from six continental ecosystems: direct ocean discharge (0.28 Pg DS/yr), endorheic basins (0.59 Pg DS/yr), cold-wet exorheic basins (0.55 Pg DS/yr), cold-dry exorheic basins (1.1 Pg DS/yr), warm-dry exorheic basins (0.82 Pg DS/yr), and warm-wet exorheic basins (4.0 Pg DS/yr), thus providing insight into the role of rainfall and temperature on continental weathering and denudation. A new, robust molar silicate/carbonate ratio of 0.42 was calculated for weathering of continental rocks, which is important in the Urey model of climate change. We estimate that rock weathering accounts for ~50% of the total solute mass flux discharged from the continents, the remainder being from externally derived marine aerosols and organic-derived bicarbonate.

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 categoriesInsufficient 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 score0.360
Threshold uncertainty score0.999

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)0.0040.002

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 head0.009
GPT teacher head0.192
Teacher spread0.183 · 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

Citations3
Published2023
Admission routes2
Has abstractyes

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