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Record W1600597455 · doi:10.7202/013200ar

Narwhal hunting by Pond Inlet Inuit: An analysis of foraging mode in the floe-edge environment

2006· article· en· W1600597455 on OpenAlexaffvenueabout
David S. Lee, George W. Wenzel

Bibliographic record

VenueÉtudes/Inuit/Studies · 2006
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsMcGill University
Fundersnot available
KeywordsForagingInletSubsistence agricultureGeographyArcticSea iceSpring (device)OceanographyFisheryArctic ice packEcologyPhysical geographyGeologyArchaeologyAgricultureBiologyEngineering

Abstract

fetched live from OpenAlex

The harvesting of narwhals by Baffin Island Inuit represents an important relationship in terms of the continuous utilization of an indigenous marine resource. However, research on Inuit hunting with respect to narwhals has been mainly confined to harvest counts despite the major role narwhals play in the local northern Baffin subsistence system. The present research examines Pond Inlet Inuit foraging behaviour for narwhals in the spring floe-edge environment. While sea ice is one of the most dominant features of the arctic marine environment for much of any year, it is in the spring that the dynamism of its physical and biological characteristics is most notable. This was especially evident at the fast ice-open water interface, or floe-edge, where rapid physical change in the condition of the ice is frequent and summer migratory marine mammals and birds are present in large numbers. In this paper, analysis of 14 observed hunts indicates that Inuit utilization of the spring floe edge for narwhal hunting, in contrast to most other hunt types, follows a sit-and-wait mode of foraging. The study also explicates aspects of Pond Inlet hunters traditional ecological knowledge necessary to travel and conduct harvesting operations successfully in this complex environment.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.908
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.046
GPT teacher head0.380
Teacher spread0.334 · 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 designQualitative
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

Citations8
Published2006
Admission routes3
Has abstractyes

Explore more

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