A Note on the Normalized Definition of Shannon’s Diversity Index in Landscape Pattern Analysis
Bibliographic record
Abstract
A common approach for quantifying landscape pattern through landscape metrics is to use categorical maps of entire landscape. However, a new interest is to use sampling data where the data are collected for only a small fraction of the entire landscape. In sample based approaches some currently used landscape metrics may not be estimated since these metrics are defined based on mapped data. Shannon’s diversity index is a frequently used metric in landscape pattern analysis. In this study, the performance of the normalized Shannon’s diversity index is demonstrated when using sampled full-coverage maps and then point sampling on the maps. Artificial and real landscapes have been employed for this purpose. The results showed that calculation of the normalized Shannon’s diversity index based on the number of land cover types in the entire classification system is more appropriate than based on the number of land cover types present within landscape. There was a strong and positive correlation between reference and estimated values but the estimator of Shannon’s diversity index was slightly and negatively biased. In conclusion, it is needed to slightly redefine some currently used landscape metrics to accommodate sampling data.
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 | 0.031 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".