Examining Intra- and Inter-annual Dynamics of Lakes of the Mackenzie River Delta, Northwest Territories, using Sentinel-1 SAR Time Series
Bibliographic record
Abstract
Under the pressures of amplified Arctic warming, developing detailed spatiotemporal understandings of Arctic surface water systems is an urgent task. This thesis establishes a method for monitoring inundation in lakes and wetlands in the Mackenzie River Delta, using Sentinel-1 SAR. The importance of dual-polarimetric entropy and speckle filtering were evaluated in a set of random forest models. Results indicated that integrating entropy and omitting the speckle filter during SAR pre-processing may be promising for surface water modelling in the Mackenzie River Delta. Analysis of lake-level inundation variability revealed spatial clusters of variable and stable lakes and suggested that lake variability is sensitive to discharge volume of the Mackenzie River. The lake-tracking framework presented in this thesis can act as an important tool for evaluating the long-term hydrological response of Arctic ecosystems, to a rapidly changing climate.
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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 | 0.000 | 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 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".