SPATIO-TEMPORAL CHANGES IN PATTERNS OF LAND USE IN POYANG LAKE DURING THE LAST DECADE
Bibliographic record
Abstract
Changes in land use in Poyang Lake from 1988 to 1998 had been explored based on the maximum-likelihood method, using remotely sensed data from Thermatic Mapper (TM) in April 1988 and April 1998. The land use was classified into seven types, forest, shrub-land, meadow, water-body, crop-land, urban-land and bareland.The matrix for land use change was obtained by using overlaying. Areas of forest, water-body and urban-land types increased, while those for cropland, meadow and shrub-land types decreased during the past decade, with a decline of 11.3% for the crop-lands and 42.7% for the meadows. At all elevations the area of both croplands and shrub-lands decreased, but that of urban-lands increased. The area of forests increased markedly and it is especially at the elevation of over 100 m. Results from an analysis on factors affecting the changes in area of land-use types indicated that precipitation controlled the area of water-body and meadow, and the policy-related factors restricted conversions of forests and shrub-lands.
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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 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".