Monitoring ice break-up on the Mackenzie River using MODIS data
Bibliographic record
Abstract
The aim of this study was to develop an approach for estimating ice break-up dates on the Mackenzie River (MR) using more than a decade of MODIS Level 3 500 m snow products (MOD/MYD10A1), complemented with 250 m Level 1B radiance products (MOD/MYD02QKM) from the Terra and Aqua satellite platforms. The analysis showed break-up began on average between days of year (DOYs) 115 and 125 and ended between DOYs 145 and 155 over 13 ice seasons (2001–2013), resulting in an average melt duration of ca. 30–40 days. Thermal processes were more important in driving ice break-up south of the MR confluence with the Liard River, while dynamically driven break-up was more important north of the Liard. A comparison of the timing of ice disappearance with snow disappearance from surrounding land areas of the MR with MODIS Level 3 snow products showed varying relationships along the river. Ice-off and snow-off timing were in sync north of the MR–Liard River confluence and over sections of the MR before it enters the Mackenzie Delta, but ice disappeared much later than snow on land in regions where thermal ice break-up processes dominated. MODIS observations revealed that channel morphology is a more important control of ice break-up patterns than previously believed with ice runs on the MR strongly influenced by channel morphology (islands and bars, confluences and channel constriction). Ice velocity estimates from feature tracking were able to be made in 2008 and 2010 and yielded 3–4-day average ice velocities of 1.21 and 1.84 m s −1 respectively, which is in agreement with estimates from previous studies. These preliminary results confirm the utility of daily MODIS data for monitoring ice break-up processes along the Mackenzie River. The addition of optical and synthetic aperture radar data from recent and upcoming satellite missions (e.g. Sentinel-1/2/3 and RADARSAT Constellation) would improve the monitoring of ice break-up in narrower sections of the MR.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".