Bridging Plant‐Breeding Gaps in Australia, Canada, and New Zealand
Bibliographic record
Abstract
Plant breeding is a multidisciplinary applied science that is crucial for enhancing food, fodder, fuel, and fiber production globally. Scientists trained as plant breeders harness many plant science disciplines to breed elite-performing varieties. However, in recent decades, several studies in the United States have shown that there are not enough plant breeders being trained to meet the demands of the growing private sector and the continuing needs of the public sector. A multidisciplinary team of scientists from Australia, Canada, and New Zealand conducted two surveys investigating the current state of the plant-breeding sector across tertiary, government, and industry levels. Their findings highlight increasing concerns about the shortage of trained plant breeders, particularly in the private sector. A coordinated approach between the public and private sectors was suggested as a strategy to improve the training for graduate plant breeders. Other suggestions for improvement included the establishment of dedicated training facilities, national funds for graduate fellowships, and increased private-sector involvement in plant-breeding education. The study raised awareness of the global shortage of trained plant breeders and provided valuable insights for decision-makers toward strategic planning to address global food and fiber production challenges. CSIRO Research Scientist Warren Conaty with cotton grower Adrian Schwager discussing cotton trials. Image courtesy of CSIRO.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".