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Record W4286587082 · doi:10.1126/sciadv.abo6493

The evolution of dog diet and foraging: Insights from archaeological canids in Siberia

2022· article· en· W4286587082 on OpenAlexaff
Robert J. Losey, Tatiana Nomokonova, Eric Guiry, Lacey S. Fleming, Sandra Garvie‐Lok, Andrea L. Waters‐Rist, Megan Bieraugle, Paul Szpak, Olga P. Bachura, Vladimir I. Bazaliiskii, N. E. Berdnikova, Natal’ia G. Diatchina, Frolov Ya., V. Gorbunov, Olga I. Goriunova, С.П. Грушин, А. В. Гусев, Лариса Геннадьевна Ярославцева, Grigorii L. Ivanov, Artur Kharinskii, Mikhail V. Konstantinov, П. А. Косинцев, Evgenii V. Kovychev, B. V. Lazin, Iurii G. Nikitin, D. Papin, А. N. Popov, Mikhail Sablin, Nikolai A. Savel’ev, A. B. Savinetsky, А. Тишкин

Bibliographic record

VenueScience Advances · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArchaeology and ancient environmental studies
Canadian institutionsWestern UniversityTrent UniversityUniversity of SaskatchewanUniversity of Alberta
Fundersnot available
KeywordsForagingArchaeologyGeographyBiologyZoologyEcology

Abstract

fetched live from OpenAlex

Research on the evolution of dog foraging and diet has largely focused on scavenging during their initial domestication and genetic adaptations to starch-rich food environments following the advent of agriculture. The Siberian archaeological record evidences other critical shifts in dog foraging and diet that likely characterize Holocene dogs globally. By the Middle Holocene, body size reconstruction for Siberia dogs indicates that most were far smaller than Pleistocene wolves. This contributed to dogs’ tendencies to scavenge, feed on small prey, and reduce social foraging. Stable carbon and nitrogen isotope analysis of Siberian dogs reveals that their diets were more diverse than those of Pleistocene wolves. This included habitual consumption of marine and freshwater foods by the Middle Holocene and reliance on C 4 foods by the Late Holocene. Feeding on such foods and anthropogenic waste increased dogs’ exposure to microbes, affected their gut microbiomes, and shaped long-term dog population history.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.050
Threshold uncertainty score0.999

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 studies0.0010.003
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 head0.007
GPT teacher head0.210
Teacher spread0.203 · 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 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

Citations19
Published2022
Admission routes1
Has abstractyes

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