Spatial–Temporal Variability of Great Slave Lake Levels From Satellite Altimetry
Bibliographic record
Abstract
The study of lake-level variability of five selected areas across Great Slave Lake (GSL) using satellite altimetry is presented. Data from Topex/Poseidon (TP) and Jason-1 (J1) missions at GSL for the ice-free seasons of 1993-2002 and 2002-2008, respectively, reveal lower performance of J1 for areas closer to 20 km from the coastline compared to 10 km for TP. A calculated bias of 6.99 cm was subtracted to J1 range since TP has better tracking of shoreline waters and lower data rejection. High correlation coefficients for the relative rate of change between lake altimetry heights (LAHs) and corresponding gauge data for Yellowknife Bay and Hay River support the use of LAH changes as effective indicators of variability at GSL. Differences in LAH between the five areas indicate a nonuniform slope which we relate more to variability of the surface water temperature distribution than wind effects. The deeper and colder areas are associated to the least change of LAH gradient through time; therefore, they represent ideal areas to study interannual climate variability. A potential correlation between areas with higher variability in LAH gradients and higher changes in modeled surface water temperatures during the 2003 ice free season is observed.
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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 | 0.001 |
| 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 | 0.000 |
| 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".