Bibliographic record
Abstract
Replicated multi-location yield trials are conducted every year in all regions throughout the world for all regionally important crops. Heritability, i.e., selection accuracy based on variety trials, improves with increased number of replicates. However, each replicate is associated with considerable cost. Therefore, it is important for crop variety trials to be optimally replicated. Based on the theory of quantitative genetics, functions that quantitatively define optimal replication on the single-trial basis and on multi-location trial basis were derived. The function on the single-trial basis often over-estimates the optimum number of replicates; it is the function on multi-location trial basis that is recommended for determining the optimal number of replicates. Applying the latter function to the yield data from the 2015-2019 Ottawa oat registration trials conducted both in Ontario and in other provinces of Canada led to the conclusion that a single replicate or two replicates would have sufficed under the current multi-location trial setup. This conclusion was empirically confirmed by comparing genotypic rankings based on all replicates with that on any two replicates. Use of two replicates can save 33-50% of field plots without affecting the selection efficacy.
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 | 0.072 | 0.150 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".