Evaluation of the advisory services provided by the Food Animal Residue Avoidance Databank
Bibliographic record
Abstract
A s part of its mission to help ensure that foods of animal origin are free of violative chemical residues, the Food Animal Residue Avoidance Databank (FARAD) offers 2 advisory services to veterinary practitioners.The first is a comprehensive online database (VetGRAM) of drugs approved by the US FDA/Center for Veterinary Medicine (CVM) for the treatment of food-producing animals.Second, FARAD offers expert-mediated advice on residue avoidance and mitigation for chemical contamination incidents and the extralabel use of drugs.This service is provided by FARAD pharmacologists and toxicologists, who can be reached by e-mail a as well as a toll-free telephone number.b VetGRAMVetGRAM is a relational database that contains regulatory information about the indications, directions for use, and withdrawal periods of drugs used in food-producing animals for therapeutic as well as production-enhancement purposes.The database is maintained and regularly updated by members of FARAD at the University of Florida at Gainesville.The first online version of VetGRAM was launched in the summer of 1999.An interface was developed that made the database searchable by species.It was made available through the Internet via the FARAD Web site, c and users could also request digital copies.Users were required to register, after which they received a user name and password that gave them access to VetGRAM.The interface has recently been updated, and a new version of VetGRAM was launched in the spring of 2003.This new version is more versatile, and the database can now be searched by any combination of species, active ingredient, trade name, drug classification, manufacturer, or new animal drug application (NADA) number.An e-mail-based survey was conducted approximately 20 months after the launch of the first version of VetGRAM.During this period, there was a mean of 50 daily hits to the FARAD Web site.Three hundred and seventy-six e-mail addresses were randomly chosen from the 1,150 subscribers to VetGRAM and compiled for the initial mailing.Of the 376 initial e-mail addresses, only 279 were found to be viable addresses.There were 143 respondents who replied to the survey (51% response rate) with 122 respondents who replied to 1 or more questions (44% adjusted response rate).The majority of respondents (86/122 [70%]) accessed VetGRAM from the United States; 12 (10%) respondents were from Canada, and 2 or more respondents were from Argentina, Australia, Mexico, Taiwan, and Turkey.Private practitioners represented the largest group of respondents (54/143 [38%]), but there were also a FARAD
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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 | 0.079 | 0.198 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.119 | 0.046 |
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".