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Record W2069576013 · doi:10.13073/fpj-d-13-00090

Industry Cycles in the US Softwood Lumber Industry: 1985 through 2010

2014· article· en· W2069576013 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueForest Products Journal · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsMinistry of Natural Resources and ForestryUniversity of TorontoOntario Forest Research Institute
Fundersnot available
KeywordsSoftwoodWood industryPulp and paper industryBusinessEngineeringCommerceForestryGeography

Abstract

fetched live from OpenAlex

Abstract Cyclical patterns in business activity are a common feature of industry in market economies. This study identifies and describes industry cycles in the US softwood lumber industry from 1985 to 2010. Statistical decomposition and filtering procedures are applied to time series data on sales volumes to extract the cyclical component, and nonparametric techniques are used to date the industry cycles. The study identifies four softwood lumber industry cycles: three coincident with business cycles and one attributable to developments in the US–Canada softwood lumber trade dispute. Softwood lumber industry cycle durations ranged between 5 and 6 years. Decline in seasonally adjusted softwood lumber industry business activity caused by cyclic contractions averaged 13 percent for the period under study, with the most recent contraction (January 2006 to March 2009) contributing a 22 percent decline in business activity.

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.

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 categoriesResearch integrity, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.085
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0020.001

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 head0.018
GPT teacher head0.251
Teacher spread0.233 · 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