Bibliographic record
Abstract
The Mackenzie Basin is composed of eight sub-basins (North Mountains, Liard, Peace, Athabasca, Great Bear Low Plains, Great Slave and Arctic Red) and includes three large lakes (Great Bear Lake, Great Slave Lake, Lake Athabasca) and three deltas (Peace-Athabasca Delta, Slave Delta, Mackenzie Delta), one of which is the world’s largest inland delta (Peace-Athabasca Delta). Annually, the Mackenzie River experiences freeze-up during the fall season and ice break-up in the spring, having an important influence on the basin hydrology Furthermore, the type of ice break-up event is dependent on the magnitude of hydrological and meteorological conditions present. In light of the decreasing network of ground-based stations operated by the Water Survey of Canada on the Mackenzie River, this study explored the use of satellite remote sensing data to improve monitoring capabilities during the ice break-up period. \n \nMODIS Level 3 snow products (MOD/MYD10A1) and MODIS Level 1B radiance products (MOD/MYD02QKM) are used to monitor ice cover during the break-up period on the Mackenzie River, Canada, for 13 ice seasons (2001-2013). The initiation of the break-up period was observed to occur between days of year (DOY) 115-125 and end DOY 145-155, resulting in average melt durations of 30-40 days. Floating ice running northbound could therefore generate multiple periods of ice-on and ice-off observations at the same geographic location. At the headwaters of the Mackenzie River, ice break-up was thermodynamically driven as opposed to dynamically, as observed downstream near the Mackenzie Delta. MODIS observations also revealed that ice runs were largely influenced by channel morphology (islands and bars, confluences and channel constriction). MODIS was found to be a powerful tool for monitoring ice break-up processes at multiple geographical locations simultaneously along the Mackenzie River. Finally, MODIS was found to be a viable tool for estimating river ice velocity where channel morphology least affected river flow. Ice run velocities north of 66° N ranged from 1.21-1.84 ms-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 | 0.000 | 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".