Bibliographic record
Abstract
In this paper substitution between the main categories of imported wood and between imported and domestic wood raw material is studied empirically using a cost-function approach and a panel data set of the world's 36 most important wood-importing countries in 19901997. A subcost function for the optimal mix of different timber inputs and estimable cost share functions are derived. The results suggest that as industrial consumption of wood continues to grow, international trade becomes an increasingly important source of wood for the world's forest industries. Cross-price elasticities of derived demand based on maximum-likelihood estimation show that substitution between different categories of imported wood in world imports is fairly low. By contrast, substitution between imported wood and domestically produced wood is higher. Long-run own-price and cross-price elasticities are larger in absolute terms than short-run elasticities. Because of fairly strong substitutability between domestic and imported wood in the long run, the effects of national forest conservation may be transferred globally via international trade.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".