Bibliographic record
Abstract
Dependable information on large-area agricultural production and production estimation are essential for agricultural markets and the formulation of national and international agricultural policies.It can provide information and technical support for regional or global food security.Factors like worldwide climate change,increasing population and fast changes in land use/cover make the need more urgent.Traditional collection of crop information depends on huge in-situ investigation,which is expensive,time consuming and vulnerable to subjective difference.Along with the development in remote sensing technology and its application to crop information acquirement,some operational crop monitoring systems were developed and put into operation by several countries and international organizations.The development of major crop monitoring systems worldwide(United States,Europe,FAO,Canada,Brazil,Argentina,Russia and India) was reviewed and introduced in detail.The paper points out that the crop acreage estimation,crop yield prediction,crop condition monitoring and drought monitoring are the four primary themes in remote sensing based crop monitoring.In crop acreage monitoring,along with the development of remote sensing technology,the dependence of these systems on field survey has not been reduced,or even increased for some reasons.This is against the primary intention of remote sensing application: to reduce or substitute field survey.The potential of remote sensing in large-area crop monitoring has not been fully exerted.Independent crop yield predicting method with remote sensing is also in great need.How to increase the role of remote sensing will be the major direction for the development of remote sensing based crop monitoring systems.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".