Operational analysis of the spatial distribution and the temporal evolution of the snowpack water equivalent in southern Québec, Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A technique for obtaining an operational regional analysis of the temporal evolution of the snowpack water equivalent in southern Québec (Canada) is proposed and implemented on a 0.1° grid. The technique combines the output of the snowpack model included in the HYDROTEL hydrological model, forced by observed temperatures and precipitations, with observed snow survey data. A strategy based on observed snow density, snowpack water equivalent and streamflow is used for model calibration. A comparison of various calibration strategies showed that the same model parameters can be used for the whole of southern Québec. It was also shown that, for operational purposes, it is sufficient to rely solely on automatic stations and to use 3 h time steps. Because snow surveys are made in deciduous forests, model parameters were adjusted to account for open areas and coniferous trees by comparing observed and simulated streamflow, using all components of the hydrological model. An assimilation technique which updates simulated water equivalent and snow density at grid points from the available snow survey data completes the operational system. An example of spring streamflow simulated using the proposed snow analysis illustrates the usefulness of the technique.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 it