Strengthening the Precision Breeding model: Identifying key trait correlations within the Canola Breeding Activity plan that could enhance resource efficiencies.
Bibliographic record
Abstract
Canola is one of Canadas leading crops and an important oilseed globally. The name Canola comes from the combination of Canada, and “ola,” which together is Canada oil. Canola must meet international chemistry standards of “… oil shall contain less than 2% erucic acid in its fatty acid profile and the solid component shall contain less than 30 micromoles of any one or any mixture of 3-butenyl glucosinolate, 4-pentenyl glucosinolate, 2-hydroxy-3 butenyl glucosinolate, and 2-hydroxy- 4-pentenyl glucosinolate per gram of air-dry, oil-free solid.” (Canola Council of Canada. 2023) Within Bayer Canada Crop Science, our canola testing program is distributed across all of Canada with the primary focus being western Canada. Within the past 5 years we have seen an increase in funding to support this critical crop from doubling our testing footprint, and through an increase in technology use such as unmanned aerial vehicles (UAV) and data science modeling. As we look at precision breeding and the positive impacts it can have for our customers by designing the best breeding strategy, we can evaluate the needs within our testing program to effectively use advancing technology. With modeling and artificial intelligence, we can also help strengthen our data algorithm to position our scientists and commercial teams with powerful data that helps uncover the “what if” scenarios. However, when do we reach over production of data? Or is there a key trait that we can gain higher insight from than others?
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".