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Record W3189854562 · doi:10.1002/evan.21921

Plant wax biomarkers in human evolutionary studies

2021· review· en· W3189854562 on OpenAlexafffund
Robert Patalano, Patrick Roberts, Nicole Boivin, Michael D. Petraglia, Julio Mercader

Bibliographic record

VenueEvolutionary Anthropology Issues News and Reviews · 2021
Typereview
Languageen
FieldEarth and Planetary Sciences
TopicArchaeology and ancient environmental studies
Canadian institutionsUniversity of Calgary
FundersSocial Sciences and Humanities Research Council of CanadaMinistry of Natural Resources and TourismMax-Planck-GesellschaftRoyal Anthropological InstituteExplorers Club
KeywordsPaleoanthropologyContext (archaeology)TaphonomyEcologyEnvironmental changeClimate changeHabitatMiddle Stone AgeGeographyArchaeologyBiologyPleistocene

Abstract

fetched live from OpenAlex

Plant wax biomarkers are an innovative proxy for reconstructing vegetation composition and structure, rainfall intensity, temperature, and other climatic and environmental dynamics. Traditionally used in earth sciences and climate studies from "off-site" ocean and lake records, biomarker research is now incorporated in archeology and paleoanthropology to answer questions relating to past human-environment interactions and human evolution. Biomarker research is generating new and exciting information on the ecological context in which Homo and its closest relatives evolved, adapted, and invented stone tool technologies. In this review, we examine plant wax biomarkers and their use in reconstructing past plant landscapes and hydroclimates. We summarize the applications of plant wax molecular proxies in archeological research, assess challenges relating to taphonomy, consider the role of modern plant ecosystems in interpreting ancient habitats, and examine case studies conducted at key paleoanthropological locations in eastern and southern Africa and Europe.

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 categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.935
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.000
Science and technology studies0.0010.004
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.080
GPT teacher head0.367
Teacher spread0.287 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations23
Published2021
Admission routes2
Has abstractyes

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