Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Variation of genotype–environment interactions can be divided to determine whether or not the interactions involve change in genotype or cultivar ranks across environments. However, no sound statistical tests are available for such determination. In this study, the restricted maximum likelihood (REML) analysis based on the mixed models theory was used to estimate genetic parameters and to test statistically for causes of genotype–environment interactions in two wheat ( Triticum aestivum L.) crosses, Potam × Ingal and RL4137 × Ingal. The data with each cross consisted of the measurements of five quantitative traits for 144 F 3 ‐derived F 5 and F 6 lines from 48 F 2 families evaluated at Saskatoon in 1986 and 1987, respectively. The causes of family × year or line × year interactions were tested by comparing log likelihoods of reduced and full models (i.e., the family or line covariance structures with and without constraints). The REML estimation guaranteed that an estimated family or line covariance matrix was positive definite. Significant line × year interactions were detected in three traits in RL4137 × Ingal only and none involved rank changes. Significant family × year interactions were found in seven of 10 cross‐trait cases, but four of those seven cases involved change in family ranks across the 2 yr. The REML analysis allows the development of sound statistical tests for the different causes of interactions and constraining estimated genetic variances and covariances within acceptable ranges, thereby effectively removing the deficiencies with the conventional multivariate analysis of variance method.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
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 it