Spatio-temporal changes in oases in the Heihe River Basin of China: 1963–2013
Bibliographic record
Abstract
Artificial oases have flourished as human activities have intensified. Changes in oases in the Heihe River Basin are typical for the arid areas of China. Based on images from 14 separate periods during 1963–2013 that were compiled from multi-sensors, data on the boundaries of oases were extracted using the methods of object-oriented image segmentation and Normalized Difference Vegetation Index thresholds as well as visual interpretation. Based on extracted data, the spatio-temporal changes in oases were analyzed using models i.e. grid transformed model for rate of change. The drivers were analyzed based on data from Statistics Yearbooks and field surveys. The results show that from 1963 to 2013 sprawl dominated oasis evolution and occurred not only in the surroundings but also in interior patches. Oasis evolution patterns of “unchanged,” “expanding,” “shrinking” and “oscillating” were observed. The development exhibited three stages, the unstable (1963–1980), the steady development (1980–2002) and the rapid expansion (2002–2013), which correspond to the Planned Economy Period, the Commodity Economy Period and the Market-oriented Economy Period, respectively, in China. Oasis expansion was mainly determined by the human instincts for survival and for human well-being and was governed by population growth, agricultural policies and economic development.
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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 | 0.000 | 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".