The importance of forestry and forest engineering: Past present future
Bibliographic record
Abstract
Until 1900, Canada had no foresters involved in logging, practising forestry or doing research. Forest engineering as a discipline held no importance whatsoever. The forest was simply exploited for its timbers and most of the forest products were sold abroad. During the next 50 years, four Canadian universities opened forestry schools, some research activities were organized by the federal government, provincial governments, and industry. However, the importance of forest engineering did not grow much. Since 1950, however, the situation was turned around completely, as was the industry. While forest operations were completely mechanised everywhere in Canada with machines or concepts often developed in the USA or in Scandinavia, more forestry schools were opened, the federal government opened forest research laboratories, provincial governments acquired more expertise in this field, and forestry equipment manufacturers did considerable development work. A national forest engineering research institute was even created. In the future, the forest community will have to team up to raise the profile of forest engineering. Key words: co-operation, forest engineering, forestry, forestry education, forestry research, sustainable management
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".