Landscape Features of Cultivated Land in Yanqing County of Beijing Municipality
Bibliographic record
Abstract
Landscape of cultivated land is an important part of agricultural landscape. The landscape characteristics of cultivated land influence directly ecological process and productivity of cultivated land. This paper studies the landscape characteristics of cultivated land in Yanqing county by examining the 18 reference areas with different cultivated land type, in order to find out the factors and mechanism affecting and controlling productivity and flow of energy and material in cultivated land. The indices of patch area, patch perimeter, patch density, perimeter density and mean patch perimeter area ratio are introduced to describe patch characteristics of cultivated land in Yanqing county. The corridor characteristics of cultivated land are expressed by the indices such as the proportion of area of all corridors to area of cultivated land, types of corridor landscape, density of corridor landscape, network connectivity of corridor landscape, networks ring of annular corridor landscape. The results show that the patches of cultivated land in the county are small and fragmental. The sizes of patches are irregular. Fragmented and divided condition of the patches by boundary are serious. Landscape granularity of cultivated land is small especially in the slope areas. The proportions of area of all corridors to cultivated land are large. Corridor types include roads, ditches and banks on the slopes. The density of roads corridor is high, but the network connectivity and ring of road corridor are poor, i.e., road corridors provide few connected and selectable line for the flows of energy and material. The density of ditches corridor is low, even there are no ditches in a few reference areas. The network connectivity of ditch corridor is also poor; therefore, irrigation guarantee and draining capacity are insufficient. The system of ditch as reflected by the characters of ditches corridor is poor. The banks in the sloping area occupy a high proportion of cultivated land, which increase the proportions of area of all corridors to cultivated land in the sloping area. This study may provide scientific basis for cultivated land consolidation and landscape planning of cultivated land in Yanqing County and other areas with similar environment.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".