Selection of Elephant-Grass Genotypes for Forage Production
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
The objective of this study was to evaluate the agronomic traits of 80 accessions of elephant grass under the soil and weather conditions of Campos dos Goytacazes/RJ, Brazil. The experimental design was set as randomized blocks with 2 replicates. The experiment continued from March 2012 to May 2013, with 5 harvests made in the dry and rainy seasons. The following traits were assessed: percentage of dry matter (%DM), dry matter yield (DMY), number of tillers per meter (NT), plant height (HGT), stem diameter (SD), leaf blade width (LBW) and leaf blade length (LBL). Data from each harvest were subjected to analysis of variance and to the Scott-Knott test (P < 0.05). Tocher’s optimization method, Mahalanobis distance, and canonical variables were utilized for the multiple traits, and the importance of the characters in the canonical variables. Genotypes with high yield were Elefante da Colômbia, Taiwan A-25, Albano, Hib. Gigante da Colômbia, Elefante de Pinda, Taiwan A-121, P241 Piracicaba, Guaçu/I.Z.2, CPAC, EMPASC 309, EMPASC 307, Australiano, and Pasto Panamá. Stem diameter (rainy season) and LBW (dry season) were the most important variables to differentiate between genotypes. There was wide phenotypic variation between genotypes, which could be divided into 15 groups by Tocher’s optimization 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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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