Bibliographic record
Abstract
To attend or not to attend, that is the question. The Pittsburgh Conference continues to pose this conundrum to conferees and exhibitors alike. This year's conference was the first to be presented without a set of paper abstracts-a good thing some would say but this old codger always used the paper abstracts to select papers of interest to our readership and to seek a full publication. The exhibit took its usual format but it seemed that there were less manufacturers present. The information presented to the attendees was also lacking and many companies' details were missing from the final program book, an omission no doubt on their behalf-my company was one of these-however I feel sure that past Pittcon organizers would have been more persistent in getting the required details for the audience. As is now the norm, many of the presentations take the form of posters displayed within the exhibition area. Without a driver to get the audience there, the traffic was slow, to say the least. Lecture presentations were also attended in a mixed fashion. So the Pittsburgh Conference show moves on, and again next year it will be held in Orlando from 12 March to 17 March 2006. No doubt I will be there making it a straight 31 in a row; in Pittsburgh Conference terms I am just a beginner with many of the attendees making more shows in a run than that. Selected abstracts dealing with topics of interest to the readers of this journal follow-hopefully many of these groups will be willing to publish their work either within this journal or elsewhere.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.402 | 0.304 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".