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Multidecadal to millennial‐scale shifts in drought conditions on the Canadian prairies over the past six millennia: implications for future drought assessment

2007· article· en· W2035375470 on OpenAlexafffundabout
Astrid Michels, Kathleen R. Laird, Susan E. Wilson, David J. Thomson, Peter R. Leavitt, Robert J. Oglesby, Brian F. Cumming

Bibliographic record

VenueGlobal Change Biology · 2007
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsUniversity of ReginaQueen's University
FundersCanada Research ChairsCanadian Foundation for Climate and Atmospheric Sciences
KeywordsAridStormPhysical geographyEnvironmental scienceClimatologyClimate changeEcologyGeologyGeographyOceanographyBiology

Abstract

fetched live from OpenAlex

Abstract Three high‐resolution climatic reconstructions, based on diatom analyses from lake sediment cores from the Canadian prairies, show that shifts in drought conditions have prevailed on centennial to millennial time scales for at least the past six millennia. These shifts in mean aridity exhibit broad regional synchrony, with particularly pronounced shifts at all sites between ∼1700–2000 cal. yr bp and ∼3600–3900 cal. yr bp , as well as at ∼5400–5500 cal. yr bp for the two sites which extend back to at least 6000 cal. yr bp . The two Saskatchewan lakes exhibited significant coherence in both the timing and direction of these shifts, whereas inferred changes at the westernmost site in Alberta were significantly correlated to the Saskatchewan sites, but opposite in sign, and exhibited more high‐frequency variation on the scale of centuries. The mechanisms behind these abrupt shifts in aridity are poorly understood, but may be linked to changes in oceanic–atmospheric interactions that influence the mean position of the jetstream and the associated storm tracks. Natural shifts in mean climatic conditions may accelerate with increasing carbon dioxide levels intensifying the likelihood of extreme droughts in North American prairies.

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)0.000
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 score0.916
Threshold uncertainty score0.940

Codex and Gemma teacher scores by category

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

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 head0.041
GPT teacher head0.335
Teacher spread0.294 · 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

Citations60
Published2007
Admission routes3
Has abstractyes

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