Evaluation of agricultural ecological environment in determining the capable areas: A case study of city of Esfahan, Iran
Bibliographic record
Abstract
The nature of different activities in production, agriculture as well as distribution and consumption section, called as expansionist activities, largely influence the ability of the land.Production of consumable material, which is required for increasing population in various areas, and their attractions make it possible to earn more profit and it causes a significant pressure on soil and water resources and can threaten environmental pollution and human food security.A self-interested attitude on land resources has led to run short-term programs without considering the ecological capability of the land.These mentioned problems are, significantly intensified particularly in arid and semi-arid areas with severe limitations of water and soil quality and quantity.Therefore, land allocation based on ecological capability and selfpurification indexes, used for land use planning, is an appropriate response to meet the deficiencies noted.This paper studies the agricultural capable lands based on land capability.The proposed study uses GIS software capabilities with application of the environmental ability evaluation model, as a holistic approach, to make sustainable development research in the region.The results indicate that suitable lands for agriculture in the whole area in different classes are widespread and with regards to dependency of more than 90 percent of people to agricultural activities, serious attention of authorities is required for providing the appropriate baseline and avoiding land use change to develop this activity.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".