MétaCan
Menu
← Back to cohort
Record W3154248679 · doi:10.33915/etd.3220

Consulting foresters of West Virginia: A profile, services and fees

2005· dissertation· en· W3154248679 on OpenAlexfundno aff
Dheeraj Nelli

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsnot available
FundersMcGill University
KeywordsWest virginiaRevenueAcreAdministration (probate law)Competition (biology)Service (business)BusinessCharge (physics)FinanceAgricultural economicsMarketingGeographyEconomicsAgricultural scienceArchaeologyPolitical scienceEnvironmental science

Abstract

fetched live from OpenAlex

Consulting foresters of West Virginia were surveyed with an aim to identify and document the firm characteristics, services offered and fees charged. The survey response rate was 56 percent. A suite of thirty-six (36) different services with different fee structures for each service is reported. West Virginia consulting foresters reported that they most commonly charge by the hour, with an exception for timber sale administration where they charge as a percentage of the sale revenues and for management plan preparation where they charge by the acre. Timber sale administration is the most frequent offered service, with an average fee of 12 percent of the timber sale revenues. Average hourly fees charged for all the services ranged from 30 to 70 dollars per hour. The two major challenges faced by the consulting foresters are cost of doing business and competition.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.149
Threshold uncertainty score0.297

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0110.003

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 head0.005
GPT teacher head0.238
Teacher spread0.232 · 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
Published2005
Admission routes1
Has abstractyes

Explore more

Same topicForest Management and Policy→French-language works237,207→