Ecological carrying capacity assessment incorporating ecosystem service flows
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 0.003 |
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 it