Bibliographic record
Abstract
This is the concluding report for the project, a continuation of research by Keyfitz and co-workers on multidimensional conservation laws, and applications of nonhyperbolic conservation laws in the two-fluid model for multiphase flow. The multidimensional research project was started with Suncica Canic, at the University of Houston and with Eun Heui Kim, now at California State University Long Beach. Two postdoctoral researchers, Katarina Jegdic and Allen Tesdall, also worked on this research. Jegdic's research was supported (for a total of one year) by this grant. Work on nonhyperbolic models for two-phase flows is being pursued jointly with Michael Sever, Hebrew University. Background for the project is contained in earlier reports. Note that in 2006, the project received a one-year no-cost extension that will end in September, 2007. A new proposal, for continuation of the research and for new projects, will be submitted in the Fall of 2007, with funding requested to begin in the summer of 2008. The reason for the 'funding gap' is Keyfitz's four-year stint as Director of the Fields Institute in Toronto, Canada. The research has continued, but has been supported by Canadian grant funds, as seems appropriate during this period.
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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".