Validating gravimetry measurements in Canada with a continental-scale hydrological database.
Bibliographic record
Abstract
Water balance simulation is a basic but essential part of large-scale hydrological modelling. Gravity data provided by GRACE (Gravity Recovery and Climate Experiment) may present an alternative, or supplement, to in situ data for verifying and supporting hydrological and glacier mass balance studies on a continental scale. This work attempts to determine the utility of GRACE data for use in large-scale mass balance calculations through an in situ hydrological database that supports hydrological mass balance calculations for major drainage basins within Canada. The development of the database is determined by the spatial and temporal scale of the GRACE data. A variety of monthly observed hydro-climatological data essential to hydrological mass balance modelling were collected for 2003, 2004 and 2005 for all of Canada (where available). GRACE estimates of average equivalent water height were computed for the Nelson River catchment in Canada. Preliminary results demonstrate that GRACE data show a seasonal cycle characteristic of snow accumulation and melt in western Canada. This cycle is strong in the foothills in the western side of the basin, but it may also leak into the gravity data from the mountainous regions outside the basin area. This signal likely dominates the summer precipitation maxima in the centre and east side of the basin.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".