Wheat Production Simulation Based on the ALMANAC Model of North China Region
Bibliographic record
Abstract
Wheat (Triticumaestivum) is one of the world's three major food crops, whose production is related to regional food security issues. Studies have shown that technological progress and climate change have a significant impact on wheat yield. We selected North China region as the study site because it is the main producer of wheat and because it experiences active climate change. Using the Agricultural Land Management Alternatives with Numerical Assessment Criteriamodel and statistical analysis method, the following factors were considered to determine the dominant factor that affects wheat production: temperature, precipitation, sunshine, and other climatic factors, mechanical power, irrigation area, chemical fertilizer amount, reservoir total storage capacity, and other technical factors.Results showed that wheat productionis affected by both climatic and non-climatic factors in North China region. Increased temperature has a positive impact on wheat production, whereas reduceds unshine has a negative effect. Warm and dry climate trends areconducive to wheat production. Mechanical tillage and fertilization, irrigation, and water conditions are conducive to the production of wheat, among which water condition has the most significant effect onwheat yield improvement. Compared withthe effects of climaticfactors, those of technical factors are more obvious and direct. In the premise of guaranteed technical conditions, the impactof climate changeonwheat production is more evidentindeveloped areas. Underdeveloped areas of wheat production are more dependent on technological progress; in particular, they rely on the use of chemical fertilizers.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".