Analysis of a wood production strategy from expert perspectives
Bibliographic record
Abstract
In 2020, Quebec adopted a strategy to increase the quantity and quality of timber it produces. During a roundtable discussion held in the fall of 2021, experts in forestry and in related fields expressed their views on the new strategy and its implementation challenges. The main purpose of this article is to present the key observations from the roundtable. The observations addressed two themes: the general context in which the strategy was developed, and the context of its implementation on the ground. Although most of the panellists agreed on the relevance of such a strategy, particularly as regards to climate change mitigation and wealth creation, several questions remain. The challenge of harmonizing uses, regionalization, spatialization of management decisions, labour shortage, and uncertain ecosystem dynamics make it difficult to assess the strategy’s potential impact on the ground and its ability to achieve its targets.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".