Bibliographic record
Abstract
Maize ( Zea mays L.) is an important food crop in the world, widely used for food, feed and industrial processing. Its growth, development and yield are highly dependent on water supply, especially in areas with insufficient precipitation or unstable climate. This study systematically explored the role of irrigation regulation in different growth stages of maize, focusing on its effects on maize physiological characteristics, yield formation and quality stability. Studies have shown that timely and appropriate irrigation can help improve photosynthetic capacity, promote root development and nutrient absorption, thereby increasing grain yield and water use efficiency. This study compared various irrigation methods such as drip irrigation, sprinkler irrigation and furrow irrigation, and explored the effects of irrigation timing and frequency on yield and resource efficiency. Through case analysis of semi-arid areas in North China and oasis agriculture in Northwest China, the application effect of regionalized and precision irrigation strategies was demonstrated, hoping to provide technical guidance for maize production in different climate zones, improve irrigation efficiency and resource utilization, and achieve the win-win goal of increasing grain production and protecting the environment.
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 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 | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".