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Record W2125443102 · doi:10.3126/init.v5i0.10260

Sawmilling in Sweden: Past, present and future

2014· article· en· W2125443102 on OpenAlexaboutno aff
Rijan Tamrakar

Bibliographic record

VenueThe Initiation · 2014
Typearticle
Languageen
FieldEngineering
TopicForest Biomass Utilization and Management
Canadian institutionsnot available
Fundersnot available
KeywordsDiversification (marketing strategy)Product (mathematics)Competition (biology)BusinessProduction (economics)Agricultural economicsEconomicsMarketingMathematics

Abstract

fetched live from OpenAlex

With 62% of her land covered with forest, Sweden is the world’s second largest combined exporter of paper, pulp and sawn wood products. Modern industrial sawmilling of Sweden started from the mid-19th Century after British import was made liberal, and Norwegian exporters faced short supplies of raw materials and Canadian producers were more expensive. During the same time, Sweden also developed industries and technology making it more competitive. However, after 20th century, sawmilling in Sweden started to adopt ‘integrated forest firm approach’ where many small and locals sawmills were forced out of the business because of numerous war-crises and oil-crises that occurred. In recent decades production and exports from Sweden is in increasing trend. This increasing trend is the result of some specific strategies undertaken by Swedish sawmilling sectors viz. transforming market channel, transforming product value adding strategy and transforming service value adding strategy. Currently, Swedish sawmills can be categorized into nine groups based on their value addition strategy and technology they use. These sawmills face competition from other materials and countries, low efficient transfer of technology and knowledge transfer, presence of small scale sawmilling, and some sawmills are in risk as they lack of product diversification and have specialized production. Future of the sawmilling in Sweden is expected to be growing. However, they have to encourage the use of wood, transfer skill and knowledge, increase the size of the firm, further diversify the products, and also improve technology and mechanization. DOI: http://dx.doi.org/10.3126/init.v5i0.10260 The Initiation 2013 Vol.5; 110-120

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.547
Threshold uncertainty score0.111

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 head0.012
GPT teacher head0.215
Teacher spread0.203 · 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 designOther design
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

Citations2
Published2014
Admission routes1
Has abstractyes

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