Meeting of New Researchers in Statistics and Probability (10th). Held in Salt Lake City, UT on July 24-28, 2007
Bibliographic record
Abstract
The Tenth Conference of New Researchers in Statistics and Probability, sponsored by the IMS, was held on the campus of the University of Utah in Salt Lake City, from July 24th to 28th, 2007. This yearly conference provides a unique opportunity for new researchers to exchange research ideas and initiate contacts amongst themselves in an informal setting as well as provide them an opportunity to interact with the invited senior participants. As part of the conference, each participant presented a talk or poster on their research. The meeting was structured to provide ample time and opportunities for them to discuss their research interests and life as new researchers over meals and a number of social activities. The majority of participants came from the United States, but also included researchers from Canada, Singapore, and Spain. The talk sessions were diverse in content, ranging from stochastic and space-time processes, semiparametric and nonparametric inference, data mining, classification and clustering techniques, methods for high-dimensional data, Bayesian methodology, statistical computing, and recent advances in survival analysis.
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 | 0.017 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.110 | 0.070 |
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".