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Record W2148749284 · doi:10.1177/0959683612437871

Species distribution modelling of ancient cattle from early Neolithic sites in SW Asia and Europe

2012· article· en· W2148749284 on OpenAlexaff
James Conolly, Katie Manning, Sue Colledge, Keith Dobney, Stephen Shennan

Bibliographic record

VenueThe Holocene · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsTrent University
FundersArts and Humanities Research Council
KeywordsDomesticationAbundance (ecology)Temperate climateGeographyBiogeographyEcologyDistribution (mathematics)ZooarchaeologyEnvironmental changeSpecies distributionRange (aeronautics)ArchaeologyClimate changeBiologyHabitat

Abstract

fetched live from OpenAlex

Species distribution models are widely used by ecologists to estimate the relationship between environmental predictors and species presence and abundance records. In this paper, we use compiled faunal assemblage records from archaeological sites located across southwest Asia and southeast Europe to estimate and to compare the biogeography of ancient wild and early domestic cattle ( Bos primigenius and Bos taurus). We estimate the contribution of multiple environmental parameters on the explanation of variation in abundance of cattle remains from archaeological sites, and find that annual precipitation and maximum annual temperature are significant predictors of abundance. We then formulate, test, and confirm a hypothesis that states the process of cattle domestication involves a change in the types of environmental ranges in which cattle exploitation occurred by applying a species distribution model to presence-only data of wild and domestic cattle. Our results show that there is an expansion of cattle rearing in more temperate environments, which is a defining characteristic of the European early Neolithic.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.030
GPT teacher head0.207
Teacher spread0.177 · 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 designSimulation or modeling
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

Citations65
Published2012
Admission routes1
Has abstractyes

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