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Record W4318262858 · doi:10.21203/rs.3.rs-2507702/v1

Winter sources of ascorbic acid for Pleistocene hominins in northern Eurasia

2023· preprint· en· W4318262858 on OpenAlexaff
Henry P. Schwarcz

Bibliographic record

VenueResearch Square · 2023
Typepreprint
Languageen
FieldSocial Sciences
TopicPleistocene-Era Hominins and Archaeology
Canadian institutionsMcMaster University
Fundersnot available
KeywordsAscorbic acidScurvyFleshVitamin CVitaminHerbivoreBiologyPleistoceneEcologyGeographyFood scienceBiochemistryPaleontology

Abstract

fetched live from OpenAlex

Abstract Hominins emerging from Africa in the Pleistocene required sources of vitamins in addition to sources of energy and substance (carbohydrates, proteins and fats). Most of their vitamin requirements could be provided by eating the flesh of herbivores but vitamin C is in low concentrations in animal muscle tissue. Lack of vitamin C causes fatal disease of scurvy. In southern Eurasia hominins would have been able to harvest fruits and vegetables throughout the year but as they migrated further to the north, they would encounter regions in which no plants were growing in mid-winter. Vitamin C is enriched in organ meats but their mass was probably too low for adequate provision. Storage of summer crops of fruit was possible. Hominins could however fulfil ascorbic acid requirements by drinking aqueous extracts from the needles of pines and other conifers which contain adequate amounts of vitamin C to satisfy human needs. We show evidence of pine needle and related consumption in Paleolithic sites.

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)0.000
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 score0.019
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.116
GPT teacher head0.420
Teacher spread0.303 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

Explore more

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