Bibliographic record
Abstract
What a great meeting in 2005! Dominique will give you a few details about the atten-dance. The research presentations were excellent. My thanks goes to those giving oral presentations. The research was interesting and you kept us on schedule. We will continue with oral presen-tations by stu-dents for the 2006 meeting. For those who did not attend the meeting, I am going to give a quick overview of the events. Friday evening began with the student presentations. These were followed by our Waksman Foundation speaker, Dr. Ralph Tanner. Dr. Tanner gave an intrigu-ing talk on research done with his stu-dents examining the bacteria in the Canadian River and other aquatic sties near Norman, OK. The organisms were identified, when possible, and their antibi-otic resistance determined. Even in pristine areas, bluegill were colonized by ciprofloxacin-resistant Enterococcus. There was an increase in multiple drug resistance in other organisms found. This raises questions about induction and occurrence of drug resistance in organ-isms found in various environments. Our
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.527 | 0.299 |
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".