Convenient Access to Archived Predictions: Canada’s CaSPAr Platform
Bibliographic record
Abstract
ydrologic, land surface, and other environmental models require meteorological input data-for example, precipitation and temperature to estimate discharge and soil moisture.In a hindcast these data are usually provided by ground observations, but in forecast mode, they require forecasted meteorological inputs from numerical weather prediction (NWP) models.Archived numerical predictions are required to evaluate or improve such forecasting systems.However, archives of NWP data are rare.Furthermore, while data are usually disseminated by the agencies producing forecasts-including the European Centre for Medium-Range Weather Forecasts (ECMWF), NOAA, and Environment and Climate Change Canada (ECCC)users of existing archives often need to download the full dataset since a spatial, temporal, and/or variable selection is not possible.There are exceptions, such as NOAA's National Operational Model Archive and Distribution System (NOMADS).In this repository of weather model outputs and inputs, and a limited subset of climate model datasets generated by NOAA, the user can select variables and specific time periods.But the download options, FTP and HTTP(S), are limited to very short archives of a few Convenient
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 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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.162 | 0.123 |
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".