RESEARCH ARTICLE Suitability of sentinel abat c a
Bibliographic record
Abstract
lec e se su im happening in the general population [1]. Sentinel surveil- can change due to environmental conditions. Though Alton et al. BMC Veterinary Research (2015) 11:37 DOI 10.1186/s12917-015-0349-1lected from each of the Belgian provinces. The criteria forof Guelph, Guelph, ON N1G 2 W1, Canada Full list of author information is available at the end of the articlelance is a strategy used to sample timely data in a rela-tively inexpensive manner rather than collect information on the general population, when population-based data collection is unfeasible in a timely or cost-effective man-ner [2]. Syndromic surveillance involves the amalgamation of signs/symptoms using data from non-traditional data sources [3]; the signs/symptoms are grouped into used less often in animal health applications than in hu-man health, sentinel surveillance has been used success-fully for surveillance in various applications. For example, following the emergence of Bluetongue virus serotype 8 in Central Europe in 2006, causing a large scale outbreak in 2007 in several countries in Europe, a Bluetongue sentinel surveillance program was established in Belgium in 2010. This surveillance program was intended to demonstrate the absence of Bluetongue virus [4]. This program ran-domly selected a total of 300 dairy herds, with 30 herds se-* Correspondence:
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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 teacher head, 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".