Evaluation of Soybean Varieties in the Northern Regional Soybean Cyst Nematode Test
Bibliographic record
Abstract
The Northern Regional Soybean Cyst Nematode (SCN) Test is used to evaluate soybean varieties produced by several public breeding programs in the northern portion of the United States and Canada. In 2018, six public breeding programs participated in the Northern Regional Soybean Cyst Nematode Test (Uniform Test 1). Public breeders can enter varieties into the SCN Uniform Test in exchange for growing locations for the test. Material entered into the SCN Uniform Test is generally in advanced stages of a breeding program. The test is an efficient method for soybean breeders to get multiple location data, in contrast with each individual program growing all of their own locations. It also produces useful information by comparing soybean lines from multiple programs and identifies lines from other states that produce well in Iowa. Results from these tests are used by soybean breeders to select varieties with superior yield and/or disease resistance to continue advancement toward variety release. These results also are used to demonstrate positive characteristics to growers and other interested parties.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".