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Record W2577609009 · doi:10.1080/03155986.2016.1270636

Quebec provincial government evaluates the potential of OR in the midst of its forest regime renewal

2017· article· en· W2577609009 on OpenAlexafffundvenueabout
Daniel Beaudoin

Bibliographic record

VenueINFOR Information Systems and Operational Research · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsUniversité Laval
FundersFPInnovations
KeywordsProcurementBenchmark (surveying)Government (linguistics)BusinessProcess (computing)Inclusion (mineral)Plan (archaeology)Cost reductionOperations managementReduction (mathematics)Government procurementEnvironmental economicsOperations researchEnvironmental resource managementComputer scienceEconomicsEngineeringGeographyMarketingMathematics

Abstract

fetched live from OpenAlex

In implementing a new forest management regime, the provincial government of Quebec is evaluating the use of operational research (OR) to develop plans. This article presents findings from one such study. The main objectives of this project were to quantify the potential reduction in wood procurement cost through (1) the inclusion of an optimization routine in the development of the annual plans and (2) a greater integration and coordination during delineation of harvest blocks and planning forest operations among companies. A wood procurement planning problem was formulated and solved as a mixed integer program. Inclusion of an optimization routine in the planning process led to a cost reduction of 0.88 $/m3 (2.6%) in comparison to the benchmark scenario. Greater integration and coordination coupled with the optimization routine reduced wood procurement costs by 1.52 $/m3 (4.5%), which translates into potential annual cost savings of $ 950, 000.

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.603
Threshold uncertainty score0.977

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.049
GPT teacher head0.339
Teacher spread0.290 · 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

Citations0
Published2017
Admission routes4
Has abstractyes

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