Estimating Elephant-Grass Adaptability and Stability for Energy-Biomass Production by Regression Models
Bibliographic record
Abstract
In Brazil, elephant grass has been researched for energy generation, as it represents an alternative energy source by virtue of its biomass production. The present study was developed to examine the adaptability and energy-biomass production stability of 73 elephant-grass genotypes under a biannual-harvest regime, using the methodologies proposed by Eberhart and Russell and Cruz. The experiment was carried out at the northern region of Rio de Janeiro State, Brazil. Nine harvests and subsequent evaluations were performed at six-month intervals. Each harvest was considered an environment of genotype evaluation. After the plants were harvested, their dry matter yield (DMY) was estimated in t ha-1 harvest. Combined analysis of variance revealed highly significant effects of genotypes, harvests, and genotype × harvest interaction, by the F test. In five of the nine evaluated harvests, the genotypes had an average dry matter yield greater than the overall mean. The method of Eberhart and Russel was effective in identifying highly adaptable elephant-grass genotypes with high dry matter production stability throughout the nine harvests. When the method of Cruz was used, no genotypes were found comprising high yielding ability, adaptability to unfavorable environments, responsiveness to environmental improvement, and high stability altogether.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".