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Simulated glacial and interglacial vegetation across Africa: implications for species phylogenies and trans‐African migration of plants and animals

2007· article· en· W1941715952 on OpenAlexaff
Sharon A. Cowling, Peter M. Cox, Chris Jones, Mark Maslin, MATHEW PEROS, Steven A. Spall

Bibliographic record

VenueGlobal Change Biology · 2007
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsLast Glacial MaximumGlacial periodInterglacialClimate changeEcologyHomo sapiensVegetation (pathology)GeographyPopulationContext (archaeology)Physical geographyGeologyPaleontologyBiology

Abstract

fetched live from OpenAlex

Abstract The paleoenvironmental context of plant and animal species evolution (including glacial migrations and population separations) is based on a very patchy and incomplete paleo‐phytogeographic record. It was our objective, therefore, to provide an additional source for paleovegetation comparison by presenting simulations from a state‐of‐the‐art fully coupled earth system model (HadCM3LC). We simulated potential paleovegetation distributions following pre‐Industrial and last glacial maximum (LGM) climate forcing for the continent of Africa. Our LGM simulations indicate that tropical broadleaf forest was not severely displaced by expanding grasslands within central Africa, although the outer extent of closed forest decreases, particularly in the north. Our simulations indicate that the structure of glacial forests may have been much different from today, in that LGM simulations indicate that forests were likely characterized by lower leaf area indexes, lower tree heights and lower vegetation carbon content. On the other hand, warmer interglacial climate (like our pre‐Industrial climate scenario) results in simulated expansion of tropical forest from coast to coast across central Africa that we postulate could have acted as a barrier to plant and animal species migrations. We suggest that our modeling experiments have implications for the interpretation of phylogenetic data, including that of our own species, Homo sapiens sapiens .

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)0.000
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 score0.030
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.074
GPT teacher head0.326
Teacher spread0.252 · 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

Citations99
Published2007
Admission routes1
Has abstractyes

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