Cartographie des géorisques karstiques à l'aide d'images radar application à l'Île d'Anticosti, Québec
Bibliographic record
Abstract
A significant of 10% of land surface constitute limestone, a fragile geological formation susceptible to rapid evolution. The evaluation of tools in recognising and analysing karst is therefore justified. The ability of imaging radar in measuring the intensity of karstification is of particular interest in this study.A method for mapping this georisk is developed. Interpretation of aerial photographs, representing the reality on the ground are compared with processed airborne radar images. In the latter, the karst is characterised by an important vertical hydrological flow. The correlation between the density of depressions and the length of waterways is poor. This due to the fact that onAnticosti island, several depressions are blocked after the glaciation and caused the numerous sinkholes to be pitched.A variable of the area occupied by the depressions is therefore taken into consideration. The images were analysed using a grid whose size is calculated based on the total number of depressions. While water surfaces, being good specular reflector, are easily spotted, pet bogs, acting as semi-specular reflectors, and the small sinkholes were often confounded with zones of low topographic backscatter present in the surrounding forest. The choice of the method of classification using a speckle-free image and texture analysis was therefore applied. The steep angle of the sensor create some important shadows areas witch used to get classified as water because both classes have similar numerical values. However, the results shows that a majority of the cells of the karstic intensity map of the radar image have a similar level of karstic intensity of those from the karstic intensity map made by airphoto interpretation.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".