Numerical Model Generated Baja Test Scenes for EarthCARE Pre-launch Studies - Part 1: Atmospheric and Surface Properties
Bibliographic record
Abstract
This first part of the dataset contains the atmospheric and surface conditions of Baja test scene (39318D) used for pre-launch studies of EarthCARE’s retrieval algorithms and data management system. The data are produced by Environment and Climate Change Canada's Global Environmental Multi-scale (GEM) NWP model (Côté et al., 1998, Girard et al., 2014). The surface albedo climatology is based MODIS’s MCD43GF 1 km resolution bidirectional reflectance distribution function (BRDF) product for the period 2002 to 2013 (Schaaf et al. 2002). Please refer to the second part of this dataset for the hydrometeor and aerosol properties. The Baja test frame is 6200 km long and 200 km wide with horizontal grid-spacing of 250 km and 57 vertical layers. The simulation is initialized at 12h00 UTC 02-Apr-2015 and saved at 21h00 UTC. This frame extends from the Canadian Arctic Archipelago, over central North America’s Great Plains and Rocky Mountains, and ends near Baja California Sur. It includes conditions of blowing snow and largely cloudless in the north end of the frame, low scattered clouds over snow-covered surfaces through the Canadian Prairies, cloudy condition over the Rocky Mountains and clear sky with some cirrus in the south end of the frame.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".