The broom of the system: a harmonized contextual data specification for One Health AMR pathogen genomic surveillance
Bibliographic record
Abstract
One Health genomics initiatives often involve data streams originating from different sources, institutions, sectors, and information management systems. These are often heterogeneous datasets structured in a variety of ways, posing challenges for data harmonization, integration and meaningful interpretation. The Genomics Research and Development Initiative Shared Priority Projects for AMR (GRDI-AMR) uses a genomics-based approach to understand the prevalence and diversity of antimicrobial resistance determinants associated with food production and different environments that can impact human health, as well as how AMR can evolve, spread, and be mitigated. This work is being carried out by six different federal government departments and agencies, academic institutions, as well as agricultural and environmental networks. To facilitate harmonization of data, a modular, interoperable contextual data (metadata) specification was developed, called the GRDI-AMR One Health specification package. The package consists of an ontology-based data standard, built using semantic best practices and existing standards, and is operationalized in a data curation tool called the DataHarmonizer. This tool automates the transformation of contextual data into NCBI’s One Health Enterics BioSample format to support public data sharing. The package also includes different kinds of support materials such as field and term reference guides and a detailed curation protocol highlighting ethical, practical and privacy considerations. Tooling and vocabulary were iteratively improved through multiple rounds of real-world testing. The data standard is continually maintained and version controlled, and has been used to resolve a variety of data harmonization issues experienced throughout numerous collaborative surveillance projects. The standard also encourages the inclusion of prevalence metrics in order to make whole genome sequencing data more useful for risk assessment, and enables communication about data needs between data generators and users. While developed for Canadian surveillance, the GRDI-AMR specification has also been implemented in international harmonization efforts, demonstrating its utility for many types of One Health genomics projects. The specification package is available at (https://github.com/cidgoh/GRDI_AMR_One_Health).
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 | 0.027 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".