Comparison of the<i>h</i>-Index Scores Among Pathogens Identified as Emerging Hazards in North America
Bibliographic record
Abstract
Disease surveillance must assess the relative importance of pathogen hazards. Here, we use the Hirsch index (h-index) as a novel method to identify and rank infectious pathogens that are likely to be a hazard to human health in the North American region. This bibliometric index was developed to quantify an individual's scientific research output and was recently used as a proxy measure for pathogen impact. Analysis of more than 3000 infectious organisms indicated that 651 were human pathogen species that had been recorded in the North American region. The h-index of these pathogens ranged from 0 to 584. The h-index of emerging pathogens was greater than non-emerging pathogens as was the h-index of frequently pathogenic pathogens when compared to non-pathogenic pathogens. As expected, the h-index of pathogens varied over time between 1960 and 2011. We discuss how the h-index can contribute to pathogen prioritization and as an indicator of pathogen emergence.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".