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Record W2592594519 · doi:10.14430/arctic4626

Farming Muskoxen for <i>Qiviut</i> in Alaska: A Feasibility Study

2017· article· en· W2592594519 on OpenAlexvenueno aff
Laura Starr, Joshua Greenberg, Janice E. Rowell

Bibliographic record

VenueARCTIC · 2017
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
FundersNational Institute of Food and AgricultureWestern SAREU.S. Department of Veterans AffairsU.S. Department of Agriculture
KeywordsLivestockGross marginArcticBusinessAgricultural scienceRevenueScale (ratio)Profitability indexAgricultural economicsEconomicsGeographyEcologyEnvironmental scienceForestryFinance

Abstract

fetched live from OpenAlex

Muskoxen (Ovibos moschatus) have been farmed since the 1960s for their fiber, called qiviut, a luxurious and highly valued underwool that is their primary insulation during the Arctic winter. Muskoxen are uniquely adapted to the Arctic. They thrive on local forages, do not require protection from the cold, and adapt well to many traditional husbandry practices. While muskoxen can be farmed for qiviut, the question remains whether it is an economically feasible and potentially sustainable enterprise in subarctic Alaska. This feasibility study was conducted using an enterprise budget for two herd sizes, 36 and 72 muskoxen, to estimate the principal costs and model different sales combinations. Under several revenue-generating scenarios, the feasibility study indicated a potential for economic viability of an established enterprise. The most profitable scenario for either herd size was selling all the qiviut as value-added yarn, coupled with livestock sales. In the absence of selling livestock, the enterprise was profitable at either scale assuming all the qiviut was sold as yarn. Selling qiviut solely as raw fiber was not projected to break even under the model parameters. The modeled enterprise emphasized the importance of value-added goods, economies of scale, low or zero opportunity costs, and the potential of a more active livestock market.

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)0.002
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 score0.457
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0080.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.089
GPT teacher head0.416
Teacher spread0.327 · 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

Citations2
Published2017
Admission routes1
Has abstractyes

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