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Record W4382918017 · doi:10.2737/fpl-rp-637

U.S. timber production, trade, consumption, and price statistics 1965 to 2005

2007· report· en· W4382918017 on OpenAlexaboutno aff
James L. Howard

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsPer capitaConsumption (sociology)Agricultural economicsProduction (economics)EconomicsBusinessGeographyPopulationDemographyMacroeconomics

Abstract

fetched live from OpenAlex

This report presents annual data but is published every 2 years. The data present current and historical information on the production, trade, consumption, and prices of timber products in the United States. The report focuses on national statistics, but includes some data for individual States and regions and for Canada. The data were collected from industry trade associations and government agencies. They are intended for use by forest land managers, forest industries, trade associations, forestry schools, renewable resource organizations, individuals in the major timber producing and consuming countries of the world, and the general public. A major use of the data is tracking industry production and consumption trends over time. One of the major shifts occurring in the wood using industry over the last 2 years is that both production and consumption of roundwood per capita have been increasing. The consumption of products per capita also increased over the last 2 years. Because of increased paper recycling and increased processing efficiency, the consumption per capita in roundwood equivalent has decreased since about 1987 from 83 ft3 to 72 ft3 per capita. In the 1960s and 1970s, consumption averaged 65 ft3 per capita. Per capita consumption in 2004 increased to 71 ft3 per capita before increasing further in 2005 to 72 ft3 per capita. Another shift occurring during 2005 is increased emphasis on wood energy use, which has shown wide fluctuations over the last decade into 2005.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.058
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0280.006

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 head0.028
GPT teacher head0.298
Teacher spread0.270 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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

Citations33
Published2007
Admission routes1
Has abstractyes

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