Bibliographic record
Abstract
Abstract The reduction of parameter dimensions in Part I is complemented by the compaction of parameter space in Part II. The range of diameters is 0.5 ≤ D ≤ 8 cm, and the assumed liquid water content varies within 1 ≤ Wf ≤ 3 for dry growth and Wf ≤ 6 g m−3 for shedding. Entirely new data throw new light onto HMT and growth. Results are as follows: (i) dry growth is unimportant, since most hailstones grow spongy; (ii) radial growth is slow for dry and fast for spongy growth because less latent heat of freezing needs to be discarded if a smaller portion of the accreted water is frozen: this growth with shedding is particularly effective if the product Y of the net collection efficiency and ice mass fraction of the deposit is 0.2 ≤ Y ≤ 0.6; (iii) the lowest possible surface temperature tS for dry growth is −32.3°C. For water-skin-covered, spongy particles tS > −5°C, and tS > −0.55°C for shedding from wet surfaces without water skins; and (iv) the interplay between water-skin thickness and surface temperature allows interconnection of all variables. However, new icing experiments are necessary to prove the proposed sphere growth by special gyration, to quantify the components of Y, and to address water-skin properties and growth. Radically redesigned dynamic cloud models need to incorporate hail packaging and rain spectra evolution in clouds. The latter will connect hailstone shedding with a warm rain process that is parallel to and interacts with hail formation.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".