OBJECT BASED THERMOKARST LAKE CHANGE MAPPING AS PART OF THE ESA DATA USER ELEMENT (DUE) PERMAFROST
Bibliographic record
Abstract
This study presents an approach to quantify thermokarst lake change and lake object structure change in spatial very high resolution remote sensing data as part of ESAs "Data User Element Permafrost". Lake Center points are used and multi temporal data is radiometrically normalized using a water mean rationing. A set of specific lake object characteristics (object shape, direction, lake object neighborhood structure and lake density) are parameterized in high resolution Rapideye data and in scanned pan-chromatic films from 1975 (Hexagon). Emphasis is on mapping of structural changes of thermokarst thaw lakes and changes of adjacent lake object properties. For this purpose specific relational neighborhood metrics are developed that quantify structural properties of the thermokarst lake areas and attributed changes. The classification is performed pan arctic on multiple test sites in Siberia, Alaska and Canada. The presented methodological approach provides a robust and transferrable concept for large scale change mapping and is important to quantify changes under potential permafrost degradation conditions. This work is part of the "Data User Element Permafrost " and is a contribution to an observation strategy for permafrost degradation. 1.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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 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".