Bibliographic record
Abstract
Abstract. Deep learning models are increasingly being applied to streamflow forecasting problems. Their success is in part attributed to the large and hydrologically diverse datasets on which they are trained. However, common data selection methods fail to explicitly account for hydrological diversity contained within training data. In this research, clustering is used to characterise temporal and spatial diversity, in order to better understand the importance of hydrological diversity within regional training datasets. This study presents a novel, diversity-based resampling approach to creating hydrologically diverse datasets. First, the undersampling procedure is used to undersample temporal data, and is used to show how the amount of temporal data needed to train models can be halved without any loss in performance. Next, it is applied to reduce the number of basins in the training dataset. While basins cannot be omitted from training without some loss in performance, we show how hydrologically dissimilar basins are highly beneficial to model performance. This is shown empirically for Canadian basins; models trained to sets of basins separated by thousands of kilometres outperform models trained to localised clusters. We strongly recommend an approach to training data selection that encourages a broad representation of diverse hydrological processes.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.192 | 0.110 |
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".