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Record W4200415775 · doi:10.1073/pnas.2107977118

Late Pleistocene shrub expansion preceded megafauna turnover and extinctions in eastern Beringia

2021· article· en· W4200415775 on OpenAlexaffabout
Alistair Monteath, Benjamin V. Gaglioti, Mary E. Edwards, Duane Froese

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsUniversity of Alberta
FundersAgence Nationale de la Recherche
KeywordsMegafaunaTundraBeringiaEcologyVegetation (pathology)SteppeShrubPleistocenePopulationClimate changeGeographyEcosystemPhysical geographyArcticBiologyArchaeology

Abstract

fetched live from OpenAlex

Significance Megafauna strongly influence vegetation structure, and population declines can alter ecosystem functioning. Overhunting of grazing megafauna is argued to have driven the collapse of widespread, northern steppe-tundra and its replacement by woody vegetation at the end of the ice age. However, in Alaska and Yukon, mammoth and horse became extinct around the time that steppe-tundra was replaced by shrub tundra, leaving it unclear whether this vegetation change caused, or was caused by, reduced megafauna populations. Comparison of accurately dated pollen records with a radiocarbon-dated bone chronology shows that shrubs began expanding before grazer populations declined. This indicates that climate was the primary control of steppe-tundra persistence and that climate-driven vegetation change may pose threats to faunal diversity in the future.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.043
GPT teacher head0.287
Teacher spread0.245 · 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

Citations42
Published2021
Admission routes2
Has abstractyes

Explore more

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