Monitoring ice break-up on the Mackenzie River using MODIS data
Bibliographic record
Abstract
Abstract. This study involves the analysis of Moderate Resolution Imaging Spectroradiometer (MODIS) Level 3 500 m snow products (MOD/MYD10A1), complemented with 250 m Level 1B data (MOD/MYD02QKM), to monitor ice cover during the break-up period on the Mackenzie River, Canada. Results from the analysis of data for 13 ice seasons (2001–2013) show that first day ice-off dates are observed between days of year (DOY) 115–125 and end DOY 145–155, resulting in average melt durations of about 30–40 days. Floating ice transported northbound could therefore generate multiple periods of ice-on and ice-off observations at the same geographic location. During the ice break-up period, ice melt was initiated by in situ (thermodynamic) melt over the drainage basin especially between 61–61.8° N (75–300 km). However, ice break-up process north of 61.8° N was more dynamically driven. Furthermore, years with earlier initiation of the ice break-up period correlated with above normal air temperatures and precipitation, whereas later ice break-up period was correlated with below normal precipitation and air temperatures. MODIS observations revealed that ice runs were largely influenced by channel morphology (islands and bars, confluences and channel constriction). It is concluded that the numerous MODIS daily overpasses possible with the Terra and Aqua polar orbiting satellites, provide a powerful means for monitoring ice break-up processes at multiple geographical locations simultaneously along the Mackenzie River.
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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 | 0.000 |
| 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".