Improving the Accuracy of Vegetation Classifications in Mountainous Areas
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In recent decades, mountainous areas that contain some of the best-preserved habitats worldwide are experiencing significant, rapid changes. Efficient monitoring of these areas is crucial for impact assessments, understanding the key processes underlying the changes, and development of measures that mitigate degradation. Remote sensing is an efficient, cost-effective means of monitoring landscapes. One of the main challenges in the development of remote sensing techniques is improving classification accuracy, which is complicated in mountainous areas because of the rugged topography. This study evaluated the 3 main steps in the supervised vegetation classification of a mountainous area in the Spanish Pyrenees using Landsat-5 Thematic Mapper imagery. The steps were (1) choosing the training data sampling type (expert supervised or random selection), (2) deciding whether to include ancillary data, and (3) selecting a classification algorithm. The combination (in order of importance) of randomly selected training data, ancillary data (topographic and vegetation index), and a random forest classifier improved classification accuracy significantly (4–11%) in the study area in the Spanish Pyrenees. The classification procedure includes important steps that improve classification accuracies; these are often ignored in standard vegetation classification protocols. Improved accuracy is vital to the study of landscape changes in highly sensitive mountain ecosystems.
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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) | 0.000 | 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 it