Modeling the distribution of cultural ecosystem services based on future climate variables under different scenarios
Bibliographic record
Abstract
With the far-reaching consequences of worldwide climate variationon ecosystems and human societies, understanding and predicting changes in cultural ecosystem services (CES) is essential for sustainable development policy and resource planning. Past studies have focused on changes and impacts on natural ecosystems, while relatively few studies have been conducted on predictions of CES. This study combines POI datasets with future climate variables under different scenarios into the Maxent model for forecasting the spatial and temporal distribution of CES, which provides strong support for future decision-making. The results indicate that: (1) Under the SSP126 and SSP585 scenarios, the CES values in the northern, western, central, and northeastern parts of the study area are relatively high, while those in the southwestern, southern, and southeastern parts are relatively low. (2) Under the SSP126 scenario, the total CES shows an increasing trend from 2021 to 2040, but slightly declines from 2061 to 2080. In contrast, under the SSP585 scenario, the total CES significantly decreases from 2021 to 2040, especially in the provinces of Guizhou, Hunan, Zhejiang, and Anhui. (3) Temperature has a significant impact on CES predictions, with the annual mean temperature (Bio1) positively correlated with total CES, contributing between 0.75 and 0.78 to the distribution of CES across different years and scenarios. Additionally, the maximum temperature of the hottest month (Bio5) and the mean temperature of the wettest quarter (Bio8) also significantly influence CES under different scenarios and years. These findings reveal the regional characteristics and variations in CES distribution under different climate scenarios, providing crucial scientific evidence for future policy-making, resource management, and climate adaptation strategies. They also offer important insights into the impact of global climate change on ecosystems and human society, serving as a valuable reference for future national decision-making.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".