BENEFITS FROM WHEAT BREEDING RESEARCH IN WESTERN CANADA
Bibliographic record
Abstract
Over the last decades, however, crop research industry has experienced significant changes in the funding and control of research. Governments are re-prioritizing research investment and directing more dollars away from crop development research toward efforts seen as more novel, further up the value chain or providing quick payoff (Meristem (2005)). Public research funding, in particular that for wheat and barley, has been declining over the past 10-15 years (Meristem (2005)). The question who should fund and control cereal research and wheat in particular is a matter of current debates. In order to propose a certain path for the government research policy it is important to know how publicly financed wheat research industry has performed until now. Past studies have provided compelling evidence that public cereal research in Canada has high investment value. A range of independent studies show a minimum 10-fold return on cereal development research (Meristem (2005)). Guzel, Furtan and Gray (2005) identified a minimum four-to-one return on investment in wheat breeding and twelve-to-one return for barley breeding. These numbers need updating and the objective of this working paper is to provide an updated estimate of the returns to wheat research in Western Canada over
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".