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Record W2070516708 · doi:10.5558/tfc83092-1

Can hybrid poplar save industrial forestry in Canada?: A financial analysis in Alberta and policy considerations

2007· article· en· W2070516708 on OpenAlexafffundvenueabout
Jay A. Anderson, Martin K. Luckert

Bibliographic record

VenueThe Forestry Chronicle · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsUniversity of AlbertaAgriculture Food and Rural Development
FundersUniversity of AlbertaMinistry of Natural Resources
KeywordsZoningTaigaBusinessForest managementResource (disambiguation)Yield (engineering)Land useForestryAgroforestryNatural resource economicsEnvironmental resource managementGeographyEconomicsEcologyEnvironmental sciencePolitical science

Abstract

fetched live from OpenAlex

Intensifying forest management has the potential to alleviate pressures of competing land uses, maintain global competitiveness in the face of increasing exotic plantations, and increase the value of the boreal forest resource. Yet, such initiatives have largely been stymied in Canada's boreal regions because intensive forest management of native tree species is not financially viable. The yield curve estimation and financial analysis conducted in this paper, however, suggests intensive management of hybrid poplar in Alberta could be financially viable. The financial viability of such initiatives will depend heavily on the policies that governments use — such as priority-use zoning — to encourage or discourage such trends. But before reforming policy, decision-makers must trade off the environmental implications of industrial plantations of exotic species with the potential gains from priority-use zoning. Key words: soil expectation value, optimal economic rotation, hybrid poplar, priority-use zoning, intensive forest management

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)0.000
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 score0.010
Threshold uncertainty score0.741

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.229
Teacher spread0.217 · 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

Citations41
Published2007
Admission routes4
Has abstractyes

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