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Record W3030442117 · doi:10.1029/2020gl087237

Lipid Biomarker Record Documents Hydroclimatic Variability of the Mississippi River Basin During the Common Era

2020· article· en· W3030442117 on OpenAlexafffund
Samuel E. Muñoz, Trevor J. Porter, Aleesha Bakkelund, Jesse Nusbaumer, Sylvia Dee, Brynnydd Hamilton, Liviu Giosan, Jessica E. Tierney

Bibliographic record

VenueGeophysical Research Letters · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTree-ring climate responses
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaNational Oceanic and Atmospheric AdministrationNational Science Foundation
KeywordsEnvironmental scienceFlood mythPrecipitationProxy (statistics)Structural basinClimatologyAridClimate changeFlooding (psychology)Growing seasonDrainage basinPaleoclimatologyHydrology (agriculture)Physical geographyGeologyGeographyOceanographyMeteorologyArchaeologyEcology

Abstract

fetched live from OpenAlex

Abstract Floods and droughts in the Mississippi River basin are perennial hazards that cause severe economic disruption. Here we develop and analyze a new lipid biomarker record from Horseshoe Lake (Illinois, USA) to evaluate the climatic conditions associated with hydroclimatic extremes that occurred in this region over the last 1,800 years. We present geochemical proxy evidence of temperature and moisture variability using branched glycerol dialkyl glycerol tetraethers (brGDGTs) and plant leaf wax hydrogen isotopic composition (δ 2 H wax ) and use isotope‐enabled coupled model simulations to diagnose the controls on these proxies. Our data show pronounced warming during the Medieval era (CE 1000–1,600) that corresponds to midcontinental megadroughts. Severe floods on the upper Mississippi River basin also occurred during the Medieval era and correspond to periods of enhanced warm‐season moisture. Our findings imply that projected increases in temperature and warm‐season precipitation could enhance both drought and flood hazards in this economically vital region.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.062
Threshold uncertainty score0.788

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.037
GPT teacher head0.289
Teacher spread0.252 · 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 teacher head, 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

Citations11
Published2020
Admission routes2
Has abstractyes

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