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Record W1591963824 · doi:10.15376/biores.6.4.4895-4908

Thirty-nine years of U.S. wood furniture importing: Sources and products

2011· article· en· W1591963824 on OpenAlexaboutno aff
William G. Luppolda, Matthew Bumgardner

Bibliographic record

VenueBioResources · 2011
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicWine Industry and Tourism
Canadian institutionsnot available
FundersUniversity of California, DavisU.S. Department of AgricultureU.S. Forest ServiceInternational Trade AdministrationU.S. Department of Commerce
KeywordsLiberian dollarChinaAgricultural economicsBusinessFurniture industryWood industryCommerceGeographyEconomicsForestryFinanceArchaeology

Abstract

fetched live from OpenAlex

In this study we analyze changes in United States imports of wood furniture over the 39-year period from 1972 to 2010. In 1972, Canada and the former Yugoslavia were the most important sources of imported wood furniture, and Europe accounted for nearly 60 percent of total imports. Shipments of low-cost wood furniture from Taiwan started to increase in the 1970s, and by 1978, Taiwan was the most important source of imported wood furniture. Overall, low-cost sources in Asia displaced Europe in 1987. Taiwan continued to be the most important source until 1994. Canada became the most important source of imported wood furniture from 1994 to 2000 as the Canadian dollar declined in value against the United States dollar. In 2001, China became the most important source of wood furniture imports. More recently furniture imports from an emerging source, Vietnam, have increased dramatically. One reason why Asian manufacturers have been so successful in the U.S. market has been that furniture consumers were influenced mainly by price. By contrast, success in some segments of the U.S.-based furniture industry indicates that models enabling consumers to make styling and pricing decisions also can be competitive.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.417
Threshold uncertainty score0.479

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 studies0.0000.000
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 head0.026
GPT teacher head0.198
Teacher spread0.172 · 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 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

Citations19
Published2011
Admission routes1
Has abstractyes

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