Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".