Spatiotemporal Patterns and Interconnections of Forest Biomass and Economic Density in the Yellow River Basin, China
Bibliographic record
Abstract
Forests are among the most diverse ecosystems on the planet, and their biomass serves as a key measure for assessing the biological productivity and carbon cycle of terrestrial forest ecosystems. Recognizing the factors that impact forest ecosystems is essential for assessing their health and developing effective conservation strategies to preserve species diversity and ecological equilibrium. This study considered forest biomass as the explained variable, economic density as the explanatory variable, and human activities, land use, and forestland protection as the control variables. Panel data encompassing 448 counties within the Yellow River Basin (YRB) for the years 2008, 2013, and 2018 were utilized as inputs for ArcGIS spatial analysis and two-way fixed-effects modeling. This approach aimed to evaluate the impact of socio-economic factors on forest biomass. The findings indicate that, (1) from both temporal and spatial viewpoints, the distribution of forest biomass in the upper reaches of the Yellow River demonstrated an improvement over the period from 2008 to 2018. Notably, in 2013, there was a significant reduction in the forest biomass distribution in the middle and lower sections, although the levels remained substantially above the average for those regions. Throughout the period from 2008 to 2018, the overall forest biomass within the YRB displayed a spatial distribution pattern, with elevated levels observed in the western areas and diminished levels in the eastern regions. (2) A one-unit increase in economic density led to a 1.002% increase in forest biomass. In the YRB, a positive correlation was observed between the economic density and forest biomass, especially in the middle and lower reaches of the river. (3) In the upstream region, forest biomass was strongly negatively correlated with cultivated land but significantly positively correlated with forest land protection. In the middle reaches, although population growth and arable land expansion led to a decrease in forest biomass, primary industry development and urbanization promoted forest biomass growth. The development of primary industries other than planting, such as the forestry industry, can contribute to the forest biomass. Moreover, in the downstream area, a strong negative correlation was observed between the number of permanent residents and forest biomass. We recommend modifications to human activities to enhance the forest biomass and the preserve forest ecosystem stability.
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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) | 0.000 | 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 | 0.001 |
| 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".