Tracking a Deadly VirusHIGHLY PATHOGENIC AVIAN INFLUENZA IN WILD BIRDS
Bibliographic record
Abstract
For Dennis Kohler, the call was somewhat unexpected. On a cold, windy morning in December 2014, Kohler sat in his Colorado office reviewing research plans for upcoming disease studies. A few hours later, Koehler's colleagues at the National Wildlife Health Center in Madison, Wis., called to notify him and other members of the U.S. Department of Agriculture's National Wildlife Disease Program (NWDP) that recent samples collected from wild birds in Washington State had tested positive for highly pathogenic avian influenza (HPAI). Earlier that month, a die-off of mallards (Anas platyrhynchos), American wigeon (A. Americana) and northern pintails (A. acuta) had occurred on Wiser Lake in Whatcom County, Wash., just south of the Canadian border near the site of HP AI outbreaks in British Columbia. State biologists had collected samples for testing, and the analysis revealed the birds likely died of aspergillosis, a common fungal infection. But what was worrisome is that they also tested positive for the Eurasian HS avian influenza virus, marking the first time a highly pathogenic Eurasian strain of avian influenza had been detected in the United States (Ip et al. 201S). Kohler knew an outbreak of HP AI in domestic turkey and chicken flocks in Canada had led the state to conduct enhanced surveillance but was still a little surprised to learn that HP AI had been discovered in wild birds. The NWDP had been monitoring and preparing for HPAI in wild birds since the 200S HPAI HSN1 scare in Southeast Asia that caused officials to kill hundreds of thousands of domestic poultry. Now the virus was confirmed in the U.S. and Kohler needed to mobilize a team of expelts to respond.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".