Evaluation on the sustainability of urbanization process based on biological footprint model
Bibliographic record
Abstract
With the rapid development of science and technology and economy, the living standard of people has tended to be higher year by year, and the degree of urbanization in China has also became increasingly higher.But the extensive economic development mode has led to the problems such as environmental pollution, waste of resources and the expansion of population.Currently one of the problems faced by China is how to find a balance between human and nature and between ecology and economy to achieve sustainable development.In this study, the sustainability of urbanization in Anhui province was evaluated using the ecological footprint model.The ecological footprint model of 2011 was analyzed in details, and the ecological footprint models of 2004 ~ 2011 were compared.The ecological footprint per capita and ecological carrying capacity were on the rise from 2004 to 2011, but there was a deficit, which increased every year.It is concluded that the use of local ecological resources in Anhui province from 2004 to 2011 has exceeded the capacity of the local environment, causing damages to the ecosystem, and the local urbanization has been in an unsustainable state and the development structure of urbanization in Anhui province is unreasonable, resulting in an increased pressure on the ecological environment and a long-term unsustainable state.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".