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Record W4412654894 · doi:10.1002/evan.70009

Human‐Dog Symbiosis and Ecological Dynamics in the Arctic

2025· review· en· W4412654894 on OpenAlexaffabout
Emma Vitale, Tatiana R. Feuerborn, Matthew Walls

Bibliographic record

VenueEvolutionary Anthropology Issues News and Reviews · 2025
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHuman-Animal Interaction Studies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsSymbiosisEcologyArcticDynamics (music)The arcticGeographyBiologyPsychologyOceanographyGeology

Abstract

fetched live from OpenAlex

Since the Late Pleistocene, humans and dogs have coevolved in the Arctic, forming a symbiotic relationship essential to survival, mobility, and adaptation. Archeological evidence shows dogs were used as traction animals by the Early Holocene, ultimately facilitating Inuit expansion and shaping Arctic settlement patterns. Despite recent declines in sled dog populations due to colonial factors, climate change, and cultural shifts, dogs remain central to Inuit identity. This paper frames the human-dog cooperation as a dynamic system of mutual learning, or enskilment, where both species acquire shared skills through collaboration. Tools like harnesses and whips serve as communicative devices within this system. Drawing on archeological and contemporary Inuit practices, the study highlights how embodied knowledge and animal agency contribute to ecological resilience. By viewing the Arctic as a co-managed landscape shaped by human-dog cooperation, the paper challenges static views of adaptation and underscores the enduring significance of this interspecies relationship.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.784
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.040
GPT teacher head0.434
Teacher spread0.394 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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