Land-Use Multicritera Evaluation Involving Carbon Sequestration Benefits Based on GIS and RS
Bibliographic record
Abstract
Terrestrial ecosystems, especially forest ecosystems, can provide significant Carbon Sequestration (CS) potential. Forestry land-use suitability analysis aims at identifying the most appropriate spatial pattern for future land uses according to specific requirements, preferences, or predictors of some activities. This paper aims at inspecting the utilization of RS, carbon models and GIS technology in regional land-use evaluation involving carbon benefits through a case study at Liping County, Guizhou Province, China. It has the following objectives: (1) to provide spatially explicit quantitative simulation of forestry land uses; (2) to use a MCDM method, i.e. the analytic hierarchy process (AHP), with GIS for comprehensive evaluation of the suitability of local forestry land use statue quo in consideration of geographical, eco-environmental and socio-economic (including carbon) indicators; and (3) to compare the evaluation results from two different multi-criteria evaluation processes with and without consideration of CS benefits.
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How this classification was reachedexpand
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) | 0.001 | 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 teacher head, 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".