The MAGS Water and Energy Budget Study
Bibliographic record
Abstract
Abstract This study represents the first attempt at developing a comprehensive climatology of atmospheric and surface water and energy budgets for the Mackenzie River basin (MRB). Different observed, remotely sensed, (re)analyzed, and modeled datasets were used to obtain independent estimates of the budgets. In particular, assimilated datasets, including the National Centers for Environmental Prediction Global Reanalysis 2 (NCEP-R2), the global 40-yr European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-40), the NCEP North American Regional Reanalysis (NARR), and the Canadian Meteorological Centre (CMC) operational regional analysis as well as results from the Canadian Regional Climate Model (CRCM) simulations, are used in the study. Apart from the development of state-of-the-art budget estimates for the MRB, the relative merits of current models, data assimilation systems, and global blended datasets in representing aspects of the water and energy cycle of this northern and data-sparse region were also assessed. In addition, the levels of uncertainty in assessing the budgets as well as their sources are discussed. The regional water budget for the MRB is closed within 10% of the observed runoff by using the moisture flux convergence from ERA-40, NARR, CMC, or CRCM. While these are noted improvements over previous water closure assessments for the region, magnitudes of the residuals in balancing the budgets are often comparable to the budget terms themselves in all the model and analysis datasets, and the spreads of budget estimates from the different datasets are also typically large, suggesting that substantial improvements to the models and observations are needed before the assessments of water and energy budgets for this northern region can be vastly improved.
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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 | 0.001 | 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 | 0.000 | 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 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".