Utilizing Conjoint Analysis to Develop Breeding Objectives for the Improvement of Pasture Species for Contrasting Environments When the Relative Values of Individual Traits Are Difficult to Assess
Bibliographic record
Abstract
Despite the large number of active programs breeding improved forage plants, relatively little is known about the weightings that breeders consciously or sub-consciously give to specific traits when selecting individual plants, or that agronomists and producers use when assessing the relative merits of contrasting cultivars. This is in contrast to most modern animal breeding programs where the relative merits of novel genetics may be assessed against an index-based breeding objective. There are numbers of reasons why these technologies have not been used widely in plant breeding although applications in forest tree breeding are relatively common. A first step in defining breeding objectives for forage species can be to define the relative importance of specific traits and to interpret how these contribute to the relative potential advantage to a new plant or cultivar. One method of defining these weightings is through surveys of users followed by analyses of their combined experience. Therefore in this study, we have assessed the usefulness of discrete choice techniques in the development of weightings for specific traits in forage plant improvement based on views of both expert users (agronomists and farm consultants) and farmers who were asked to define their relative priorities when considering the renovation of a pasture. The surveys were conducted in three distinct regions of, or environments within, Australia of special relevance to meat production from beef and sheep (high rainfall, temperate (inland), and Mediterranean). In summary this study defines the focus of breeding objectives and selection criteria for different pasture species across production systems.
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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 | 0.026 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".