‘Big Data’ in animal health research – opportunities and challenges
Bibliographic record
Abstract
Automated systems for high-input data collection and data storage have led to exponential growth in the availability of information. Such datasets and the tools applied to them have been referred to as 'big data'. Starting with a systematic review of the terms 'informatics, bioinformatics and big data' in animal health this special issue of AHRR illustrates some big-data applications with papers on how the use of various omics methods may be used to facilitate the development of improved diagnostics, therapeutics, and vaccines for foodborne pathogens in poultry and on how a better understanding of rumen microbiota could lead to improved feed absorption while minimizing methane production. Other papers in this issue cover the use of big data modeling in dairy cattle for more effective disease interventions and machine learning tools for livestock breeding. The final two reviews describe the use of big data in better vector-borne pathogen forecasts with canine seroprevalence maps and modeling approaches to understand the transmission of avian influenza virus. Although a lot of technical and ethical issues remain with the use of big data, these reviews illustrate the tremendous potential that big-data systems have to revolutionize animal health research.
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".