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Record W3093763280 · doi:10.1029/2020gl088728

African Humid Period Precipitation Sustained by Robust Vegetation, Soil, and Lake Feedbacks

2020· article· en· W3093763280 on OpenAlexafffund
Deepak Chandan, W. R. Peltier

Bibliographic record

VenueGeophysical Research Letters · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsUniversity of Toronto
FundersCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada
KeywordsPrecipitationClimatologyNorthern HemisphereMonsoonVegetation (pathology)Environmental scienceInsolationPeriod (music)Anomaly (physics)SteppeClimate changeAtmospheric sciencesPhysical geographyGeologyGeographyMeteorologyOceanography

Abstract

fetched live from OpenAlex

Abstract The African Humid Period ( ∼ 11,000–5,000 years before present) was the most recent of several precessionally paced wet intervals during which an increase in the Northern Hemisphere summer incoming solar radiation intensifies the West African Monsoon leading to dramatic changes over northern Africa. However, insolation anomaly alone is not sufficient and feedbacks are essential for further amplification of the monsoon. The most significant feedbacks derive from the land surface, arising from changes to vegetation, soil properties, and distribution of surface water. We show that in contrast to previous studies that have explored the individual impacts of these feedbacks, a modern climate model yields a much greater increase in precipitation in response to their collective effect. Agreement with proxies is improved while the desert‐steppe transition is pushed further northward than in any previous study. In the West African Sahel, intensities of summer daily mean and extreme precipitation increase by 150% and 90%, respectively.

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)0.001
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 score0.080
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.029
GPT teacher head0.267
Teacher spread0.239 · 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

Citations112
Published2020
Admission routes2
Has abstractyes

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