Supplementary material to "The Great Lakes Runoff Intercomparison Project Phase 4: The Great Lakes (GRIP-GL)"
Bibliographic record
Abstract
Comparison of gridded ERA5-Land SWE estimates and ground-truth SWE observations from CanSWE database The ERA5-Land SWE estimates (Muñoz Sabater, 2019) were compared with the SWE observations available in the version 2 of the CanSWE dataset (Vionnet et al., 2021).CanSWE combines historical manual (snow surveys) and automatic SWE measurements collected across Canada by different provincial and territorial agencies as well as hydro-power companies.CanSWE is an evolving dataset.Please refer to the following repository to check for version updates of the dataset: https: //10.5281/zenodo.4734371.In this study, only manual SWE measurements were used to make sure that the snow data would be available over the period from 2000 to 2017.In Ontario and Quebec, manual SWE data are collected on a biweekly to monthly basis.The times series of ERA5-Land SWE estimates were extracted at the grid cells corresponding to the snow observation locations available in CanSWE.Only stations with at least 34 observations (i.e., on average at least two observations per year during the study period from 2001 to 2017) were kept in the analysis.A total of 272 stations was considered and the results are shown in Fig. S1. Figure S1.Comparison of ERA5-Land and CanSWE snow water equivalent estimates.Panel (A) displays the spatial distribution of the Kling-Gupta efficiency (KGE) between the CanSWE observations (obs) and ERA5-Land (sim) estimates at the N = 272 snow observation locations available in the CanSWE dataset with at least 34 observations (i.e., on average at least two observations per year during the study period from 2001 to 2017).Panel (B) summarizes these KGE values by a cumulative distribution function showing that around 83% of the stations have medium performance, 57% are good, and 14% show an excellent performance.ERA5-Land shows good performances (KGE larger than 0.65) at 57% of the stations (Fig. S1B).These stations are mostly located in the Ottawa River watershed, in the Lake Superior watershed and in the northern part of the Lake Huron watershed (Fig. S1A).These regions are characterized by the largest mean winter SWE in the Great Lakes regions (Fig. 2 in the main
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.750 | 0.281 |
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".