Bibliographic record
Abstract
Abstract. The Earth Cloud, Aerosol and Radiation Explorer (EarthCARE) mission was launched on May 28, 2024. One of the most exciting new measurement capabilities of the EarthCARE mission is the CPR Doppler velocity measurement. The availability of Doppler measurements from space will offer a unique opportunity for the collection of a global dataset of vertical motions in clouds and precipitation. An important step in realizing this opportunity is to evaluate the CPR Doppler velocity measurements against those collected by surface-based observatories. Validation with two high-latitude surface-based Doppler radar observatories demonstrates that the CPR Level-2 Doppler velocities exhibit minimal biases (within a few cm/s), especially in ice clouds. Even in low-level mixed-phase clouds, the CPR’s Doppler velocity measurements provide reliable values, although careful consideration is needed for specific limitations such as vertical smoothing effects due to the radar’s pulse length. Despite the inherent challenges associated with space-based Doppler measurements, these results suggest strong potential for the EarthCARE mission to provide unprecedented global climatological insights into hydrometeor sedimentation velocities.
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 | 0.039 |
| 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 | 0.017 |
| Insufficient payload (model declined to judge) | 0.110 | 0.093 |
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".