Large Sample, High Dimension Hydrology Dataset Validation: Getting Bit By Bytes
Bibliographic record
Abstract
Many large hydrometeorological datasets have been developed and published in recent years in support of wide applications from physical to machine learning models, and from operations forecasting to prediction in ungauged basins. The HYSETS database (Arsenault et al. 2019) is one such large-sample dataset featuring numerous physiographic, geologic, and climate attributes associated with over fourteen thousand monitored watersheds in North America and Mexico. The wide array of geospatial data sources used to extract the many basin attributes described by this dataset, combined with the continental scale of study regions, necessitates the assembly of geospatial data sources with non-uniform properties and the analysis of observations collected by different governing organizations. In this study, the static basin attribute set derived for the HYSETS database was replicated. Preliminary results suggest that incorporating updated geospatial data sources such as higher resolution DEM, and the interpretation of basin attribute derivations due to the use of different software packages, can yield distinct estimates of statistical properties of basin attributes with implications for their use as model input data. At the very least, the preliminary results demonstrate that the greater the size and complexity of a dataset, the greater the likelihood of introducing bias and computational error.
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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 | 0.006 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.010 |
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".