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Record W2158155683 · doi:10.1139/x05-065

Costs and regional impacts of restoration thinning programs on the national forests in eastern Oregon

2005· article· en· W2158155683 on OpenAlexvenueno aff
Darius M. Adams, Gregory S. Latta

Bibliographic record

VenueCanadian Journal of Forest Research · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsThinningSubsidyAgricultural economicsWelfareEconomicsDeadweight lossEconomic impact analysisForestryBusinessNatural resource economicsGeographyMicroeconomics

Abstract

fetched live from OpenAlex

An intertemporal spatial equilibrium model of the eastern Oregon softwood log market was employed to estimate the market and economic welfare impacts of restoration thinning programs established on national forests in the region. Programs treated only lands with sawtimber thinning volume and varied by the extent of public subsidies for costs, the types of costs that could be subsidized, and the form of the subsidy payment. Impacts on private harvest timing, numbers of mills, and postprogram log prices in the region were found to vary markedly with the form of the program. Log consumers (lumber mills) consistently realized relatively large surplus gains, while private log producers' surplus showed smaller but consistent losses. For a comparable subsidy budget, programs that subsidized only hazard removal costs, on sites where net unsubsidized sawtimber returns promised to be less that these costs, led to a larger area treated than did programs with more flexible subsidy conditions. Across all programs, net agency receipts from sawtimber sales were estimated to be insufficient to cover the costs of all areas in need of thinning treatment (lands with and without sawtimber thinning volume).

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)0.002
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 score0.510
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.083
GPT teacher head0.342
Teacher spread0.259 · 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

Citations36
Published2005
Admission routes1
Has abstractyes

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