COMMENTARY / COMMENTAIRE Plant biology and food science in Canada: a vision for the future
Bibliographic record
Abstract
undertake a reallocations exercise designed to provide support for new initiatives and emerging fields of research in a period of fiscal restraint. NSERCs Grant Selection Committees (GSC) were instructed to establish committees charged with preparing reports that respond to the question Why is it important for Canada that your research community should receive some of the funds available for reallocation? The Plant Biology and Food Science Grant Selection Committee (GSC03) responded by striking a committee representing the breadth of research it supports. Extensive input was obtained from the scientific community through a web site and electronic discussion group. A discussion document was then prepared and distributed to 34 consultants representing NSERC-funded researchers and leaders in the agricultural, food, and forest industries. After refining the report, a second draft was circulated to more than 90 scientists for further review. This process provided us with a collective vision of our discipline that focuses on the importance of fundamental research, guided by excellence, innovation through multidisciplinary approaches, international leadership potential, and relevance to Canada and Canadians. To achieve this vision, NSERC must introduce a strategy to attract, train, and retain our best young minds, establish a broad, realistic funding base, and create opportunities for more interaction among disciplines. Six specific recommendations were put forward to achieve these goals with a total request for $15.3 million in reallocated and new money. If funded, this initiative will provide GSC03 researchers with the support required to generate the ideas, communicate the insights, develop the skills, and educate the personnel that will be essential for Canadas participation in the biotechnology
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 | 0.010 | 0.063 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.016 | 0.011 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.009 | 0.004 |
| Research integrity | 0.056 | 0.061 |
| Insufficient payload (model declined to judge) | 0.013 | 0.008 |
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".