An examination of value-added and variable cost trends across Canadian forest regions and sectors
Bibliographic record
Abstract
Value-added forestry has received much attention in the past few decades as a means of generating more output value for a given timber input. The growth in value-added production in the forest industry is directly affected by changing forest output/input prices, technology, investment strategies, government policies, and a host of other factors. Since many of these factors are unique to each forest region and sector in Canada, it is expected that one would observe a number of different value-added and variable cost trends over time. The purpose of this paper is to examine value-added and variable cost trends in five regions and three sectors of the Canadian forest industry over the 197095 period in order to shed light on those that are the most promising. A seemingly unrelated regressions technique is employed to the data set and findings reveal that a number of sectors and regions have exhibited favourable value-added/variable cost trends. Others, however, have not. Suggestions are made as to where future industry investments and government policies might be directed to aid in the development of value-added. Key words: Canadian forest industry, value-added, variable cost, profit, regional analysis, seemingly unrelated regressions
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".