Bibliographic record
Abstract
The study mainly summarizes some key factors and optimization methods for improving energy corn biomass yield. Appropriate planting density and row spacing can significantly increase the aboveground yield of corn. But how to plant it depends on the corn variety and local climatic conditions, and it cannot be a one-size-fits-all approach. In addition, the amount of nitrogen fertilizer and the time of harvest are also very important. Applying more nitrogen fertilizer appropriately and choosing the right time to harvest can increase the yield and biomethane output. From 1983 to 2017, thanks to new breeding techniques and increased planting density, corn biomass yield increased by about 30%. Among them, breeding has a greater impact on yield than planting density. In heavily polluted soils, applying some humic acid can also help, which can significantly increase corn dry matter yield and energy output. If the management input is high, such as enough fertilizer, the biomass yield will also be higher; however, with moderate input, the energy utilization rate may be better. Some new technologies now, such as remote sensing combined with crop models, can also help us more accurately estimate corn yields over a large area. This is very useful for adjusting field management. If you want to increase the yield of energy corn, it is very important to choose the right variety, arrange the planting density, manage the fertilizer, grasp the harvesting time, and add the help of some modern technology.
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 | 0.000 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".