Root-Soil Interactions Affecting Maize Growth
Bibliographic record
Abstract
The relationship between the roots of corn and the soil is of great significance to the growth, nutrient absorption and stress resistance of the crop. Many studies have shown that the morphology of roots, their secretions, and their interactions with the soil environment and microorganisms can affect corn's utilization efficiency of key resources such as water, nitrogen, and phosphorus, as well as its yield. Root secretions not only improve the environment around the roots but also attract beneficial microorganisms, assist in nutrient cycling, and make the soil healthier. Some agricultural practices, such as precise fertilization, adding soil conditioners, and cultivating varieties with better root systems, can also enhance the "root-soil interaction" effect, thereby improving the stress resistance and resource utilization rate of corn. In the future, if high-throughput phenotypic technology of root systems, soil science, microbiomics and agronomy are combined, it will be able to provide more assistance in cultivating high-yield, stress-resistant and sustainable corn varieties and planting methods. A thorough understanding and application of root-soil interaction are of vital importance for food security and sustainable agricultural development. The purpose of this study is to summarize and analyze these aspects to provide references for subsequent corn improvement and agricultural management.
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 | 0.000 | 0.000 |
| 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".