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Record W4360826141 · doi:10.1038/s41467-023-37165-w

Alkalinity responses to climate warming destabilise the Earth’s thermostat

2023· article· en· W4360826141 on OpenAlexaff
Nele Lehmann, Tobias Stacke, Sebastian Lehmann, Hugues Lantuit, John Gosse, Chantal Mears, Jens Hartmann, Helmuth Thomas

Bibliographic record

VenueNature Communications · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsDalhousie University
FundersBundesministerium für Bildung und ForschungDeutscher Akademischer Austauschdienst
KeywordsAlkalinityThermostatEarth (classical element)Global warmingEnvironmental scienceClimate changeNatural resource economicsClimatologyEcologyAtmospheric sciencesBiologyChemistryEconomicsGeologyThermodynamicsPhysics

Abstract

fetched live from OpenAlex

Abstract Alkalinity generation from rock weathering modulates Earth’s climate at geological time scales. Although lithology is thought to dominantly control alkalinity generation globally, the role of other first-order controls appears elusive. Particularly challenging remains the discrimination of climatic and erosional influences. Based on global observations, here we uncover the role of erosion rate in governing riverine alkalinity, accompanied by areal proportion of carbonate, mean annual temperature, catchment area, and soil regolith thickness. We show that the weathering flux to the ocean will be significantly altered by climate warming as early as 2100, by up to 68% depending on the environmental conditions, constituting a sudden feedback of ocean CO2 sequestration to climate. Interestingly, warming under a low-emissions scenario will reduce terrestrial alkalinity flux from mid-latitudes (–1.6 t(bicarbonate) a−1 km−2) until the end of the century, resulting in a reduction in CO2 sequestration, but an increase (+0.5 t(bicarbonate) a−1 km−2) from mid-latitudes is likely under a high-emissions scenario, yielding an additional CO2 sink.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

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 communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.058
GPT teacher head0.343
Teacher spread0.285 · 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

Citations21
Published2023
Admission routes1
Has abstractyes

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Same venueNature Communications→Same topicGeology and Paleoclimatology Research→French-language works237,207→