Utilisation d'une classification structurale OASIS pour la cartographie d'unités de paysage dans une région représentative du Liban
Bibliographic record
Abstract
To construct an environmental database that is homogeneous both spatially and in land attributes, terrain units are produced from the analysis of satellite imagery enhanced by the inclusion of thematic layers in the analysis. The work is carried out on a representative region of Lebanon, covering a region from the coastal plain through the mountains inland. The interpretation of satellite images (Landsat TM) is based on a structural classification of the terrain by the software package OASIS that characterizes every pixel by a vector sum of the pixels in its neighbourhood. To add thematic content to the polygons defined by the analysis of the satellite images, geographic information system (GIS) thematic maps, such as morphology, drainage density, land cover, geology, and soils, are used. The information from these maps is entered in the polygons from the satellite images using three rules to allow synthesis of the different elements of the landscape: (1) dominance rule — a given terrain polygon is characterized by the thematic unit that is dominant in the area; (2) unimodality rule — if, on a large terrain polygon, a bimodal population exists for a theme, it is divided into two new polygons; and (3) scarcity conservation rule — if, on a large terrain polygon, there is a theme occupying a small area that does not exist elsewhere, it is saved in a new polygon. This approach to classification of the land results in the division of the 955-km2 study area into 10 homogeneous units. These units will be of significant help when studying the characteristics of the land for other purposes.
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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 | 0.001 |
| 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 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".