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Record W2119836658 · doi:10.5558/tfc2014-009

Considerations for boreal mixedwood silviculture: A view from the dismal science

2014· article· en· W2119836658 on OpenAlexaffvenueabout
Glen W. Armstrong

Bibliographic record

VenueThe Forestry Chronicle · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsSilvicultureForest managementBorealTaigaAgroforestryBusinessLiberian dollarForestryNatural resource economicsEnvironmental scienceEconomicsGeographyFinance

Abstract

fetched live from OpenAlex

Since the end of the twentieth century, there have been some notable changes in the economic climate facing forest products companies operating in the boreal mixedwood forest in Canada: low product prices, a strong Canadian dollar, and increasing recognition of the importance of non-timber forest values are major challenges that must be faced by forest managers and provincial governments. In response to these challenges, foresters and governments may need to rethink the objectives of forest management as stated in policy, and to rethink the silviculture prescriptions applied to the forest. This rethinking may well lead to a forest with less annual production (at least in terms of softwood volume), but with greater economic value for timber production. I present results of financial analysis of several alternative management scenarios for the mixedwood component of the Boreal Plains ecozone, and conclude that only very low cost silvicultural prescriptions make sense when silvicultural expenditures are viewed as an investment in future stands. If these silvicultural expenditures are viewed as a cost of current harvesting, they may be large enough to turn a profitable timber harvesting opportunity into a money-losing one.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.324
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.015
GPT teacher head0.246
Teacher spread0.231 · 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.

Study designNot applicable
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

Citations27
Published2014
Admission routes3
Has abstractyes

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