Diurnal Cycle of Raindrops Size Distribution in a Valley of the Peruvian Central Andes
Bibliographic record
Abstract
In the Central Andes of Peru, convective and stratiform rainfall occurs, frequently associated with convective storms. The raindrop size distributions (RSD), measured by a Parsivel-2 optical disdrometer, were characterized by the variation of their normalized parameters. The RSD dataset includes measurements corresponding to 18 months between 2017 and 2019. As a result, it was found that the mass-weighted mean diameter Dm and the Nw parameter present respectively high and low values, in the interval of 15–20 LST (local standard time), wherein deeper and more active clouds appear. The events including convective rainfall contribute 67.5% of the accumulated total, wherein 92% corresponds to the 15–20 LST interval. It is concluded that the spectral variability of the RSD is strongly controlled by the cloudiness configuration field developing over the west (convection over highlands) and east (convection over Amazon) sides of the valley. In the afternoon, clouds develop and drift to the east, over the Andean valleys and towards the Amazon, intensified by local orographic circulation. The opposite happens at night, when the stratiform rainfall is dominant and it is controlled by clouds, located in the Inter-Andean valley, generated by the convection fields formed over the Amazon forest.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".