Bibliographic record
Abstract
Abstract. The Earth Cloud, Aerosol and Radiation Explorer (EarthCARE) satellite consists of active and passive sensors whose observations will be acted on by an array of retrieval algorithms. Earth-CARE’s retrieval algorithms have undergone pre-launch verifications within a virtual observing system that consists of 3D atmosphere-surface data produced by the Global Environmental Multi-scale (GEM) NWP model, and instrument simulators that when applied to NWP data yield syn-thetic observations for EarthCARE’s four sensors. Retrieval algorithms operate on the synthetic observations and their estimates go into radiative transfer models that produce top-of-atmosphere solar and thermal broadband radiative quantities, which are compared to synthetic broadband measurements thus mimicking EarthCARE’s radiative closure assessment. Three high-resolution test frames were simulated; each measures ~6,200 km along-track by 200 km across-track. Hori-zontal grid-spacing is 250 m and there are 57 atmospheric layers up to 10 mb. The frames span wide ranges of conditions and extend over: i) Greenland to The Caribbean crossing a cold front off Nova Scotia; ii) Nunavut to Baja California crossing over Colorado’s Rooky Mountains; and iii) central equatorial Pacific Ocean that includes a mesoscale convective system. This report discusses how the test frames were produced and presents their key geophysical features. All data are publicly available and, owing to their high-resolution, could be used to simulate observations for other measurement systems.
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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.023 | 0.021 |
| Insufficient payload (model declined to judge) | 0.147 | 0.113 |
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".