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Record W3094276601 · doi:10.1017/qua.2020.76

A 3000-year record of vegetation changes and fire at a high-elevation wetland on Kilimanjaro, Tanzania

2020· article· en· W3094276601 on OpenAlexaff
Colin J. Courtney Mustaphi, Rahab N. Kinyanjui, Anna Shoemaker, C.T. Mumbi, Veronica Muiruri, Laura Marchant, Stephen M. Rucina, Rob Marchant

Bibliographic record

VenueQuaternary Research · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsQueen's University
Fundersnot available
KeywordsWetlandHolocenePhytolithCharcoalVegetation (pathology)EcologyPhysical geographyMacrofossilPaleoecologyGeographyRadiocarbon datingGeologyPollenArchaeologyBiology

Abstract

fetched live from OpenAlex

Abstract Kilimanjaro is experiencing the consequences of climate change and multiple land-use pressures. Few paleoenvironmental and archeological records exist to examine historical patterns of late Holocene ecosystem changes on Kilimanjaro. Here we present pollen, phytolith, and charcoal (>125 μm) data from a palustrine sediment core that provide a 3000-year radiocarbon-dated record collected from a wetland near the headwaters of the Maua watershed in the alpine and ericaceous vegetation zones. From 3000 to 800 cal yr BP, the pollen, phytolith, and charcoal records show subtle variability in ericaceous and montane forest assemblages with apparent multicentennial secular variability and a long-term pattern of increasing Poaceae and charcoal. From 800 to 600 cal yr BP, montane forest taxa varied rapidly, Cyperaceae abundances increased, and charcoal remained distinctly low. From 600 yr cal BP to the present, woody taxa decreased, and ericaceous taxa and Poaceae dominated, with a conspicuously increased charcoal influx. Uphill wetland ecosystems are crucial for ecological and socioeconomic resilience on and surrounding the mountain. The results were synthesized with the existing paleoenvironmental and archaeological data to explore the high spatiotemporal complexity of Kilimanjaro and to understand historical human-environment interactions. These paleoenvironmental records create a long-term context for current climate, biodiversity, and land-use changes on and around Kilimanjaro.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score1.000

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.075
GPT teacher head0.302
Teacher spread0.228 · 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; both teacher heads agree on what is shown here.

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

Citations12
Published2020
Admission routes1
Has abstractyes

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