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Record W2116780410 · doi:10.1177/0959683608100575

Holocene climate variability and vegetation dynamics inferred from the (11700 cal. yr BP) Laguna Rabadilla de Vaca sediment record, southeastern Ecuadorian Andes

2009· article· en· W2116780410 on OpenAlexaff
Holger Niemann, Torsten Haberzettl, Hermann Behling

Bibliographic record

VenueThe Holocene · 2009
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsUniversité du Québec à Rimouski
FundersUniversität BremenDeutsche Forschungsgemeinschaft
KeywordsHoloceneVegetation (pathology)PollenGeologyPhysical geographyHolocene climatic optimumEcologyGeographyOceanography

Abstract

fetched live from OpenAlex

Palaeoenvironmental changes, inferred from a 492 cm long lake sediment core from Laguna Rabadilla de Vaca (3312 m) in Podocarpus National Park, southeastern Ecuadorian Andes, were investigated using multiple proxies. Pollen, spore and charcoal analyses, as well as x-ray fluorescence and magnetic susceptibility scanning reflect the last c. 11 700 cal. yr BP of climate and vegetation history. Pollen data indicate that the herb-paramo was the main vegetation type at Laguna Rabadilla de Vaca during the early-Holocene period, before c. 8990 cal. yr BP. The herb-paramo was rich in Poaceae, Cyperaceae, Valeriana and Huperzia, reflecting cold and relatively wet climatic conditions. During the middle Holocene from c. 8990 to 3680 cal. yr BP Weinmannia increases markedly, indicating warmer climatic conditions than present-day, probably related to the Holocene thermal optimum, because of a spread of shrub-paramo vegetation and/or a shift of mountain rainforest and sub-paramo vegetation zones to higher elevations. XRF data indicate a drier period from c. 8990 to 6380 cal. yr BP and a wetter period from c. 6380 to 3680 cal. yr BP. A Poaceae-dominated herb-paramo occurred from c. 3680 cal. yr BP until modern times, reflecting cooler climatic conditions relative to the middle Holocene. XRF and charcoal data indicate a decrease in precipitation during this period.

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)0.002
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 score0.024
Threshold uncertainty score0.490

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.013
GPT teacher head0.237
Teacher spread0.224 · 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

Citations43
Published2009
Admission routes1
Has abstractyes

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