Correlations Between Stability Statistics of Forage Production in Elephant Grass
Bibliographic record
Abstract
Elephant grass (Pennisetum purpureum Schum.) is an important forage plant in the tropics and the potential of genotypes depends on the genotype × environment interaction effects. The objective of this study was to evaluate and compare different stability methods of forage production of 53 elephant grass genotypes, in Campos dos Goytacazes, Rio de Janeiro State, Brazil. The experiment lasted two years, a total of ten cuts with randomized block experimental design with two replications. The analysis of variance was applied to data from dry matter production (DMP), subjected to stability analysis using the following methods: Yates and Cochran, Plaisted and Peterson, ecovalence Wrickie, Kang and Phan, Lin and Bins, and Annicchiarico. The Yates and Cochran method showed more stable genotypes but being less productive. Plaisted and Peterson and ecovalence Wrickie methods presented a Spearman correlation equal to 1, so it is not recommended to implement them concurrently. Lin and Bins showed a strong negative correlation with the average being a method that indicates the genotype also very stable and productive. This method correlates with Annicchiarico, which also indicates productive genotypes by the confidence index. The genotypes most stable among the methods were: Pusa Napier 2, Taiwan A-143 and Merckeron Comum.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.001 | 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".