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Record W2072696180 · doi:10.1139/x05-008

Testing for variation in the western Oregon softwood log price structure

2005· article· en· W2072696180 on OpenAlexvenueno aff
Ståle Størdal, Darius M. Adams

Bibliographic record

VenueCanadian Journal of Forest Research · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsSoftwoodWageEconometricsAgricultural economicsEconomicsMathematicsQuality (philosophy)Convergence (economics)StatisticsBotanyLabour economicsBiologyMacroeconomics

Abstract

fetched live from OpenAlex

Timber owners in western Oregon have been concerned about the erosion of price premiums for higher quality grades of Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) sawlogs over the past decade. Time series tests indicate that the ratio of 3Saw (lower quality) to 2Saw (higher quality) sawlog prices did rise over the 1990–2000 period, suggesting convergence between the prices. To identify causes of this trend, we estimate reduced form equations for Douglas-fir sawlog prices with time-varying coefficients using flexible least squares. Log grade prices were related to prices of lumber by grade, prices of chipped residues, labor wage rates, and volumes of public timber supplied. Changes in the relation of log grade prices are reflected through changes in both reduced form coefficients and levels of the exogenous variables. Changes in the coefficients, in turn, may derive from shifts in the distribution of log qualities within grade categories and from grade-specific changes in sawing and log production technologies. Coefficient trends showed that higher quality lumber grades became more important for 3Saw logs during the sample period, while lower quality lumber grades and chips became more important for 2Saw, moving the log grade prices closer together. Comparison of simulated 2Saw and 3Saw prices with and without historical time patterns in the exogenous variables had little impact on their relationship, suggesting that factors shifting the coefficients may have been the primary drivers of price convergence.

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)0.001
metaresearch head score (Gemma)0.006
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.871
Threshold uncertainty score0.257

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.046
GPT teacher head0.312
Teacher spread0.265 · 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

Citations2
Published2005
Admission routes1
Has abstractyes

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