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Record W2789618992 · doi:10.14214/sf.6999

The Law of One Price in global coniferous sawlog markets

2018· article· en· W2789618992 on OpenAlexaboutno aff
Virginia Morales Olmos, Jacek P. Siry

Bibliographic record

VenueSilva Fennica · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicArt History and Market Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsEconomics

Abstract

fetched live from OpenAlex

With progressing globalization of forest production, roundwood prices in different countries may follow similar trends. The Law of One Price (LOP) postulates that the price of a similar product should be the same in different markets when expressed in the same currency. The objectives of this research were (1) to test the LOP in selected coniferous sawlog markets, and (2) to analyze whether a common market â the European Union â leads to the existence of a single sawlog market. The analysis included Brazil, Chile, Finland, Germany, Norway, Poland, Russia Northwest, Sweden, the US South, the US Northwest, Canada East, and Canada West. The results suggest that some of the coniferous sawlog markets were cointegrated which means that they shared a long-term relationship even if in the short-term they do not necessarily adjust to each other. The LOP may hold between coniferous sawlog markets in Sweden and Norway from 1995 through 2012 when sawlog prices were expressed in USD, and in Norway and Finland for 2001â2012 for prices in EUR. Furthermore, the LOP may hold for North American markets in the West for 2004â2012.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0020.008
Scholarly communication0.0050.008
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.000

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 head0.019
GPT teacher head0.226
Teacher spread0.208 · 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 source (direct Gemma or distilled Codex), 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

Citations6
Published2018
Admission routes1
Has abstractyes

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