Landscape metrics as indicators of the structural landscape changes – two case studies from the Czech Republic after 1948
Bibliographic record
Abstract
The composition and configuration of landscape elements as well as their size and shape co-determine the character of the flows and processes in the landscape. Using remote sensing data and landscape metrics, this article sets out to analyse changes in the landscape structure at two different spatial scales, focusing on two study areas in the Czech Republic in the latter half of the twentieth century. To compute the landscape metrics, Patch Analyst 3.0 software was applied (Sustainable Forest Management Network and the Centre for Northern Forest Ecosystem Research, Ontario Ministry of Natural Resources). Considering the number of individual patch types and their degree of diversity over an approximately 50-year time period: 1948–1982–1990–2005 (as well as for comparison of Patch Analyst results with CORINE land cover data on a larger spatial scale, 1990–2000), the most sensible approach would be to focus explicitly on analysing the results obtained through Patch Analyst.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".