Ecological carrying capacity assessment incorporating ecosystem service flows
Bibliographic record
Abstract
Ecological carrying capacity focuses on the limits of human development, serving as a policy instrument for guiding regional sustainable development. Regional space exhibits openness and dynamics. Nonetheless, ecological carrying capacity assessments seldom account for the potential influence of dynamic elements. The endeavor to integrate ecosystem service flows into ecological carrying capacity assessment represents an innovative approach to address this issue. This paper reviews multiple methods of assessing ecological carrying capacity and highlights the deficiencies in representing dynamic elements. Subsequently, the research progress in ecosystem service flows is examined, encompassing connotations, features, and models. Based on common theories, intermediary linkages, and the impact of incorporating spatiotemporal dynamics, the relationship between them is analyzed. The advantages of ecosystem service flows are also elucidated, which provide explicit spatial information and integrate biophysical processes when representing dynamic elements. The framework for ecological carrying capacity assessment incorporating ecosystem service flows comprises five steps: key theory selection, objectives and scope establishment, identification of supply and demand matching and assessment of flow utility, ecosystem service flow analysis, and ecological carrying capacity assessment. In the future, the research will focus on conducting quantitative pilot projects in typical regions, removing barriers to ecosystem service flows, and developing a dynamic ecological carrying capacity assessment model that considers multiple factors.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".