Potential productivity and yield estimation of spring wheat based on a Net Primary Production model——Taking Baiyin district in Gansu province as an example
Bibliographic record
Abstract
Based on the Carnegie Ames Stanford Approach(CASA),a net primary productivity(NPP) model for spring wheat was established with 250 m × 250 m MODIS remote images and weather station-based meteorological data in the Baiyin District at wheat growing season from April to July 2010. Through calculation on the transform relationship between NPP and dry matter,the potential productivity of spring wheat was estimated. The results showed that the NPP and potential productivity of spring wheat in south area were both greater than those in the north area of Baiyin District. The minimal value of NPP was 42 g C·m-2·a-1and the maximal value was 402 g C·m-2·a-1. In the meantime,it was found that the production potential of spring wheat showed clear correlations with seasons. According to the actual wheat yield of per unit area and the potential productivity of spring wheat,the estimation model by regression analysis was established. Further tests were carried out to evaluate the accuracy and utilization of the model with a root mean square error(RMSE) at 76. 33 g·m-2and a relative root mean square error(RMSEr) at 23. 51%.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".