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Record W1968201339 · doi:10.1080/14942119.2014.957527

Developing training for industrial wood supply management

2014· article· en· W1968201339 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Forest Engineering · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsSupply chainContext (archaeology)Training (meteorology)Engineering managementSupply chain managementSupply and demandProcess managementBusinessEngineeringOperations managementKnowledge managementComputer scienceMarketingEconomics

Abstract

fetched live from OpenAlex

An understanding of supply chain management is a prerequisite for efficient supply operations. This paper presents the structure of training currently used in Sweden to prepare master’s-level foresters for managing wood supply operations. Based on a basic framework of professional tasks, eight key learning outcomes are targeted; one focuses on raw material requirements, three on securing supply, three on enabling delivery, and one on control and coordination. Sixteen exercises are used to meet the eight learning outcomes. An overview of the exercises is presented as well as the pedagogical approach used. Current training is focused on developing student understanding of the industrial context as well as competences and skills required to solve typical professional tasks. The paper concludes with a discussion of further development opportunities including a coupling of tasks and learning outcomes with applicable operations research methodology.

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.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.886
Threshold uncertainty score0.349

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 studies0.0000.000
Scholarly communication0.0000.000
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 head0.027
GPT teacher head0.250
Teacher spread0.223 · 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