Forecasting future consumption of coniferous wood in India: a quantitative approach
Bibliographic record
Abstract
Over the last few years, Canada has been very successful in increasing its trade in wood products with China. India however, still remains an elusive market. There is a large amount of peer reviewed literature on the specifics of the Indian wood market, and the potential for trade in softwood products. Whereas the majority of studies describe in great detail the opportunities and constraints in dealing with India, very little quantitative information is available about the trends and patterns that determine the Indian wood market. This study uncovered and described one such trend by identifying the relationships between the level of imports of softwood products and such factors as India's Gross Domestic Product (GDP), domestic production, the price of lumber on international markets, tariffs, and the price of Teak logs as a substitute for softwood products. This study analyzed 13 years of quarterly data using the ordinary least square regression technique. Diagnostics were conducted using Akaike and Schwartz criterions, the Durbin-Watson test, and the Breusch-Pagan-Godfrey test for heteroscedasticity. Results suggest that the indicated variable collectively explain 74% of variability in import levels. Two variables in particular, real GDP and the price of Teak have a significant, positive impact on the level of imports of softwood products with 0.45 and 0.49 as respective elasticities. Continuing growth of India's GDP will ensure an ever increasing demand for imported wood products in the years to come. To maximize this opportunity, North American exporters should not compete with New Zealand's low quality pine, but should instead focus on competing with dark coloured tropical hardwoods that are becoming prohibitively expensive as world wide supplies of Teak and other tropical hardwoods continue to diminish. --P. i.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".