Bibliographic record
Abstract
Symposium presented at Case School of Law (Franklin Thomas Backus School of Law) on Mar. 31, 2006 by the Frederick K. Cox International Law Center and co-sponsored by the Institute for Global Security Law and the Law-Medicine Center Keynote address: Lawrence Gostin (John Carroll Research Professor at Georgetown University Law Library); participants: Theodore R. Wasky (FBI), Stuart Rifkind (FEMA), Tom O'Hara (Plain Dealer), Josh Meyer (Los Angeles Times), Captain Kenneth J. Baca (Medina County, Ohio, Sheriff's Office), Melvin R. House (Ohio Emergency Management Agency), Leah C. Dorman (DVM, Ohio Department of Agriculture, Division of Animal Industry), Kathleen O'Malley (U.S. District Court, N.D. Ohio), Gloria Mintah (Legal Services, Canadian Food Inspection Agency) "The Fifth Plague" is a unique simulation-based counter-terrorism conference dealing with a bioterrorism attack. Participants, who include local, state, national and international officials, will be asked to "role play" their response to an agricultural-based attack. Issues that will be addressed include: legal questions of authority and powers of various agencies; cooperation among different branches of the government and between the U.S. and Canada; and responses to an actual attack."-- program brochure
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.462 | 0.173 |
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".