Senior Ian Koziara Advances to Regional Level of Metropolitan Opera National Council Auditions
Bibliographic record
Abstract
It is on to St. Paul, Minn., for Lawrence University senior Ian Koziara. The voice performance major from Itasca, Ill., was one of five singers from the recent Wisconsin district selected by judges to advance to the second round of the Metropolitan Opera National Council Auditions. A tenor, Koziara next competes at the Upper Midwest Region auditions Feb. 1, 2014 in St. Paul for the opportunity to sing in New York City for the Met’s national semifinals. Koziara was among 36 singers from throughout the Midwest who competed in the 52nd edition of the annual district auditions Oct. 19 at the Sharon Lynn Wilson Center for the Arts in Brookfield. A student in the Lawrence Conservatory of Music voice studio of Teresa Seidl, Koziara received $2,000 as a district winner. Justin Berkowitz, a 2011 Lawrence graduate, also competed in the Wisconsin District Met auditions and was awarded a $500 Appreciation Award by the judges. The Metropolitan Opera National Council Auditions are designed to discover promising young opera singers and assist in the development of their careers. The auditions are held annually in 13 regions of the United States and Canada. There are 40 districts within these regions, providing opportunities for singers to enter the auditions at the local level.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.275 | 0.131 |
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".