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Record W4389194792 · doi:10.1177/09596836231211878

Holocene histories of biome stability in northern Amazonian savannas

2023· article· en· W4389194792 on OpenAlexaff
Julian Beltran, Mauro B. de Toledo, Michael Palace, Jack E. Dibb, Mark B. Bush

Bibliographic record

VenueThe Holocene · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicAmazonian Archaeology and Ethnohistory
Canadian institutionsGlobal Institute for Water SecurityUniversity of Saskatchewan
Fundersnot available
KeywordsHolocenePollenBiomeAmazonianCharcoalPhysical geographyPaleoecologyPaleoclimatologyPalynologyClimate changeEcologyBefore PresentGeographyAmazon rainforestGeologyEcosystemArchaeologyBiology

Abstract

fetched live from OpenAlex

Paleoecological analysis of three lake sediment cores from the Roraima savannas in northern Brazil revealed systems dominated by Poaceae pollen throughout most of the Holocene. A slight increase of palms and woody taxa, probably linked to wetter conditions, is observed during the last 1000 years but is more noticeable after 300 and 150 calibrated years BP (yr BP) respectively. Charcoal was present throughout affirming the importance of fire for the landscape but showed the highest values mostly in the last millenium. Poaceae pollen size spectra varied considerably, showing more variability than the overall pollen record. Despite evidence of some climate change in the Holocene, these savannas were stable systems. The sedimentary records showed gaps in deposition between 10,000 and 7800 yr BP and between 2500 and 1200 yr BP, suggesting dry periods during which lakes most likely dried out or became impermanent and highlighting the sensitivity of these lakes to local water table variation. The establishment of the modern climate in the region is evident after 1000 yr BP.

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 score0.024
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.051
GPT teacher head0.243
Teacher spread0.193 · 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

Citations4
Published2023
Admission routes1
Has abstractyes

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