Replication Data for the manuscript "Using an ensemble of artificial neural networks to convert snow depth to snow water equivalent over Canada" submitted to HESS
Bibliographic record
Abstract
This dataset contains most of the snow measurement data and meteorological data available across Canada and needed to replicate the study by Ntokas et al. (2020) and submitted to HESS. Important notes: Data from the Ministère du Développement durable, de l’Environnement et des Parcs (MELCC) of the province of Québec is freely available for university research, but to have access to it you will need to fill out a short form explaining the purpose of your research. This form presents the terms of use of its data and it also allows the MELCC to gather statistics on the usage of its data. The entire dataset includes the data from all governmental partners, BUT excludes that from private companies, as we do not have permission to share it. It is already formatted to facilitate its use with our codes. The later are also available upon request, on GitHub (https://github.com/konstntokas/Hydrology_ANN_SD2SWE) (2020-10-28)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.002 | 0.000 |
| 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 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".