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Record W1872921161 · doi:10.7202/1015954ar

Economies of driftwood: Fuel harvesting strategies in the Kodiak Archipelago

2013· article· en· W1872921161 on OpenAlexvenueno aff
Jennie Shaw

Bibliographic record

VenueÉtudes/Inuit/Studies · 2013
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArchaeology and ancient environmental studies
Canadian institutionsnot available
FundersOffice of Polar ProgramsNational Science Foundation
KeywordsArchipelagoFirewoodSubarctic climateContext (archaeology)Subsistence agricultureGeographySubsistence economyArcticArchaeologyAgroforestryEcologyEnvironmental scienceAgriculture

Abstract

fetched live from OpenAlex

Fuelwood harvesting is an integral part of the subsistence regime for many Arctic and subarctic peoples. Despite the relative paucity of woody resources in the northern tundra, charred wood fragments recovered from archaeological sites reveal a harvesting practice that is thousands of years old. Indeed, fuelwood gathering is a strategic behaviour involving a complex set of decisions beyond merely harvesting by proximity, as some have proposed. In this research, fuelwood harvesting is modeled within an economic framework. A fuel value index (FVI) is established to quantify the energetic returns of different wood species, and ethnographic interviews with Kodiak Island residents demonstrate the knowledge context that surrounds firewood acquisition. Archaeological charcoal from Kodiak Archipelago sites showcases a flexible, though increasingly selective strategy of fuelwood use by early inhabitants. For 7,500 years, maritime hunter-gatherers in the Gulf of Alaska took advantage of wood patchiness; they used a combination of exotic coniferous species in the form of driftwood and native deciduous trees such as alder to fuel their steam baths, smokehouses, and homes.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.388
Threshold uncertainty score0.620

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 studies0.0000.001
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 head0.036
GPT teacher head0.245
Teacher spread0.210 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations13
Published2013
Admission routes1
Has abstractyes

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