Poplar research in Canada — a historical perspective with a view to the futureThis minireview is one of a selection of papers published in the Special Issue on Poplar Research in Canada.
Bibliographic record
Abstract
This paper provides a brief history of the development of poplar research in Canada within the broader North American context, as background to the present collection of papers on current Canadian poplar research. After the earliest times and European settlement, a few individual scientists played a pioneering role in early selection and breeding of poplars. The development of farm shelterbelts in the prairies over the last 100 years, including the widespread distribution of adapted poplars, has had a significant impact on the landscape. In the last 30 years, industrial strategies for the development and use of poplars have been the most important driver for poplar research. All of these components have in some way foreshadowed the present dramatic leading-edge research in poplar genomics. With the increasing diversity and sophistication of poplar research, particularly in recent years, a need is seen for identification of research priorities and coordination of research activities by disparate parties.
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.024 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".