A Sustainable Land Utilization Pattern for Confirming Integrity of Economic and Ecological Objectives under Uncertainties
Bibliographic record
Abstract
In this study, an integrated crop–forest system with market approach (ICFM) associated with recovering forest and withdrawing cultivation was developed for confirming regional integrity of economic and ecological objectives under uncertainties. A mixed quadratic stochastic-fuzzy programming method (QSF) was proposed for planning an ICFM issue under uncertainties. QSF can not only deal with spatial and temporal variations of meteorological condition, but also handle uncertainties expressed in terms of probability distributions and fuzzy sets. Meanwhile, it can also tackle nonlinear relationships between land resource plan and economic data. The developed QSF was applied to an ICFM issue in Xixian county, China. The results of adverse effects from irrigation, ecological effects from forest, land utilization with market approach and optimal system benefits were obtained. It can facilitate policymakers to adjust current land utilization with market approach to improve the productivities of land resources. The tradeoff between crop irrigation and forest protection can prompt generation of optimized plans with consideration of economic and ecological objectives, which can be availed to generate strategies for confirming integrity of socio-economic and eco-environmental sustainability.
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.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".