Exploratory Landscape Metrics for Agricultural Sustainability
Bibliographic record
Abstract
Socio-economic growth and urban change have been an increasing concern for decision makers in recent decades. The monitoring, mapping and analysis of agricultural land-use change, especially in areas where urban change has been high, is crucial. The collision between traditional economic activities related to agriculture in tourist areas such as the Algarve and current demand for tourism infrastructures in urban regions is also leading to loss of economic activity. This paper uses a combined Geographical Information System approach with CORINE land cover datasets to perform a Shannon’s diversity index quantifying changes in agricultural areas. The paper then expands on the nature of the agricultural changes observed, and offers a multi-temporal assessment by means of landscape metrics in order to understand the shifting land-use patterns for the Algarve in land use planning and regional economic equilibrium: (1) forest regions become transformed into agricultural areas and agricultural areas become urban; (2) areas which are initially agricultural become scattered residential regions created by economic investors; and (3) agricultural land-use changes have a cyclical nature in which – in the course of the economic recession – such dynamic effects brought about a decrease in tourism and focus on traditional sectors. Keywords: agricultural land use change, sustainable agricultural development, landscape metrics, agricultural land loss, Geographical Information Systems
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".