Snow-cover variations over the Mackenzie River basin, Canada, derived from SSM/I passive-microwave satellite data
Bibliographic record
Abstract
Abstract The Mackenzie GEWEX Study (MAGS) is a Canadian investigation that has the objective of understanding and modelling the water and energy cycles and their roles in the climate system in the high-latitude Mackenzie River basin, including assessing potential changes due to climate variability and change. The Climate Research Branch (CRB) of the MeteorologicalService of Canada has investigated snow-cover variations over the MAGS region using snow water equivalent (SWE) datasets derived from Special Sensor Microwave/Imager (SSM/I) passive-microwave satellite data for the winter seasons 1988–98. The SWE datasets were derived using four CRB algorithms for prairie, coniferous-forest, deciduous-forest and sparse-forest land-cover types and then evaluated against available in situ SWE measurements for MAGS subbasins. Overall, the SWE algorithms produce reliable estimates (within 10–20mm of in situ SWE measurements) for the validated part of the MAGS region, although some areas exhibit underestimations of > 30 mm, which may be due to the presence of a high density of lakes or a decreased microwave sensitivity to high-SWE conditions (>100mm). A time-series dataset of SSM/I-derived SWE for1 March of each year from 1988 to 1998 has been produced as a MAGS deliverable, which provides important information on the spatial and temporal variability in snow cover over the Mackenzie River basin. This dataset has been used in the assessment of snow-cover outputs from MAGS hydrological and climate-modelling investigations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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 teacher head, 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".