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Record W3144017878 · doi:10.33915/etd.7689

Family Forest Owners Satisfaction with Timber Transactions

2020· dissertation· en· W3144017878 on OpenAlexfundno aff
Jeffrey M. Lee

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsnot available
FundersMcGill University
KeywordsBusinessForestryAgricultural economicsAgroforestryNatural resource economicsGeographyEnvironmental scienceEconomics

Abstract

fetched live from OpenAlex

Forest industries in the eastern U.S. rely heavily on family forest owners to supply fiber needs for their mills. As of 2017, 79 percent of West Virginia is classified as forestland and of this roughly 86.5 percent is privately owned. With such a heavy reliance on wood from private forest lands, family forest owner satisfaction is extremely important if companies want to continue harvesting or working with these landowners in the future. Timber transactions are complex. No two timber transactions are exactly the same. They often involve many different parties apart from the landowner. Site conditions, land cover, and landowner goals all are major factors that influence the outcome of a timber harvest. A timber harvest can leave a property completely transformed. For better or worse the property will not be the same as before the harvest. Timber harvests are common in West Virginia many lack the use of a forester. Without a forester, landowners are likely at a competitive disadvantage when negotiating timber contracts and accomplishing their goals and future of their properties. The goal of this study was to explore ways to alleviate common pitfalls that lead to legal or financial issues that are associated with timber transactions. We carried out a mail-based survey to landowners who had recently harvested timber from their West Virginia properties. In this paper, we explore the relationships between landowner satisfaction with a harvest, their property attributes, management goals, and the types of professional assistance they received during their timber transaction. Many attributes selected to represent conditions and events during timber transactions were found to be significantly related to the overall satisfaction of landowners following timbering operations. The adequacy of several of the attributes were used as indicators of landowners’ perception of service quality. Having a forestry professional assist with the timber sale enhanced the likelihood that landowners would be satisfied with timbering outcomes.

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)0.003
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 score0.035
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.008
GPT teacher head0.220
Teacher spread0.212 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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