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Record W2789560767 · doi:10.24124/2006/bpgub1321

Sustaining investment in the northern British Columbia forest industry: rates of return and cost of capital

2006· dissertation· en· W2789560767 on OpenAlexaffabout
Frederick Fominoff

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
KeywordsEarningsRate of returnWoodchipsBusinessCost of capitalReturn on capitalAgricultural economicsFinanceEconomicsNatural resource economicsFinancial capitalProfit (economics)EngineeringPulp and paper industryCapital formation

Abstract

fetched live from OpenAlex

Forest resources and the forest industry have played a key role over the last 90 to 100 years in providing a livelihood to the people settling in northern British Columbia.Pulp mills were attracted to the region because of the availability of residual woodchips from area sawmills.The two segments now share the cost of timber harvesting through the sale of residual chips from sawmills to pulp mills.In order to be financially sustainable the industry must generate returns that compensate the providers of financial capital.To test whether this is occurring in the Northern British Columbia forest region two firms were selected for study as a proxy for the industry.During the five-year period 2000-2004 the lumber assets of one firm earned a return slightly lower than its cost of capital while the lumber assets of the second firm generated returns exceeding its cost of capital.Panel returns exceeded required returns.Pulp earnings however, were significantly below required returns for both firms.

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)0.010
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 score0.120
Threshold uncertainty score0.241

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0050.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.232
Teacher spread0.224 · 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
Published2006
Admission routes2
Has abstractyes

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