Bibliographic record
Abstract
Abstract For more than 20 years, the AABB and the American Red Cross (ARC) have hosted the Immunohematology Reference Laboratory (IRL) Conference in mid-spring. Initially the conference was jointly hosted by the two organizations; it has since been hosted alternately, with each organization choosing the host city and planning the conference. The conference has been held in various cities throughout the United States, including Atlanta, Chicago, Las Vegas, New Orleans, Memphis, and this year, Orlando. The 2007 IRL Conference will be hosted by the AABB and held in Albuquerque, New Mexico, the weekend of March 27 to 29, 2007. The conference begins on Friday afternoon with proctor-led case studies discussing advanced immunohematologic investigations, followed by a welcoming reception that promotes connecting and networking with fellow technologists and physicians from around the country and, typically, Canada. Saturday begins with breakfast followed by speaker presentations on various serologic, technical, clinical, administrative, quality, and regulatory issues that affect today’s reference laboratories. These presentations extend until late afternoon with scheduled breaks and a provided lunch. The presentations continue on Sunday with the conference ending at noon. Attendees are encouraged to bring posters for viewing and those that do have the opportunity to present the information to all. Following are summaries of the presentations given at the 2006 IRL conference that was hosted by the ARC from April 28 to 30 in Orlando, Florida.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.118 | 0.064 |
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".