Correlation between landscape pattern changes and carbon intensity in Nanjing
Bibliographic record
Abstract
It is of great significance to carry out research on landscape patterns for urban planning and economic development. Through the supervised classification of Nanjing’s remote sensing images, the transfer matrix and landscape metrics of different land-use types were calculated, and the landscape pattern changes in Nanjing from 2004 to 2020 were analyzed. Meanwhile, the carbon sink coefficient of each land use and the carbon emission coefficient of different types of carbon sources were used to calculate the total carbon emissions. The changes in carbon emissions in Nanjing were analyzed in combination with the changes in landscape patterns. The results are as follows: (1) The main types of land use in Nanjing are cropland and construction. The area transfer of different types of land is mainly manifested in the transformation of cropland into construction. The overall landscape fragmentation is gradually increasing, showing a trend of dispersion, complexity, heterogeneity and diversification. (2) The total carbon emissions in Nanjing gradually increased, and the total carbon sink amount decreased first and then increased, finally returning to the level of carbon sink in 2006. Nanjing attaches great importance to low-carbon construction and has achieved effective emission reduction. The energy structure of Nanjing has begun to shift to efficient and clean energy. (3) The carbon intensity in Nanjing is decreasing steadily. There is a significant correlation between the carbon intensity and landscape pattern changes in Nanjing.
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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".