Estimation des retombées économiques directes engendrées par le réseau de création de valeur de la filière bois de feuillus durs au Québec
Bibliographic record
Abstract
Deciduous hardwood species represent more than 17% of the total fibre used in primary wood processing mills in Quebec. However, economic outcomes from primary and secondary hardwood processing are not well documented. Meanwhile, many questions arise regarding the growing difficulty of good access to the fibre in both quantity and quality. The main objective of this study is to define the hardwood network. To do so, we have to quantify the economic outcomes by developing a method of evaluation of the outcomes induced by the industries of 2nd and 3rd transformation, compare that network with the softwood network and then, finally, make a sensitivity analysis of these outcomes when facing variation in the level of exports and the average sale price. The results show that in 2002, the value of production of the hardwood processing industry, all levels combined, was 2.3 billion dollars. The presence of a 2nd and 3rd transformation industry can allow an increase of more than double the value of production. Hardwood sawmills generate direct economic outcomes similar to the softwood industry but at a smaller production scale. The sensitivity analysis showed that a decrease of 5% in exports of the 1st transformation products would generate a growth of 3% of the value of the total deliveries and increase the total number of employees of the 2nd and 3rd transformation industry by 9%. Key words: deciduous hardwood, first and second transformation, exports, direct economic outcome, employment
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".