Bibliographic record
Abstract
Boiling is a heat-transfer process during which vapor bubbles are created on a heated surface (nucleate boiling) or inside overheated liquid (bulk boiling). Boiling has been used by humans for tens of thousands of years for cooking, however, its application in industry started somewhere in the seventeenth century. Moreover, actual research into boiling-heat-transfer phenomena started only around 1920s. In general, several major types of boiling process can be identified: natural-convection pool boiling vs. forced-convection flow boiling and nucleate boiling vs. bulk boiling. Major nucleate-pool-boiling characteristics are as the following: Onset of Nucleate Boiling (ONB); Heat Transfer Coefficient (HTC); Critical Heat Flux (CHF); HTC at film pool boiling; minimum heat flux at film pool boiling; and HTC at transition boiling. Quite similar characteristics correspond to flow-boiling: Onset of subcooled Nucleate Boiling (ONB); Onset of Significant Void (OSV); HTC; CHF; and Post-DryOut (PDO) heat transfer. In spite of more than 100 years of active research and many years of applications, boiling phenomena/heat transfer are still not fully investigated and understood. There are some attempts to develop boiling-phenomena theories, but, unfortunately, they are not so practical yet. Therefore, more or less all practical calculations of various boiling characteristics/parameters rely heavily on empirical correlations, which were obtained experimentally. Due to this sophisticated studies are performed into boiling phenomena in the world.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.007 |
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".