The Use of Filter-feeders to Manage Disease in a Changing World
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Rapid environmental change is linked to increases in aquatic disease heightening the need to develop strategies to manage disease. Filter-feeding species are effective biofilters and can naturally mitigate disease risk to humans and wildlife. We review the role of filter-feeders, with an emphasis on bivalves, in altering disease outcomes via augmentation and reduction. Filtration can reduce transmission by removing pathogens from the water column via degradation and release of pathogens in pseudofeces. In other cases, filtration can increase pathogen transmission and disease risk. The effect of filtration on pathogen transmission depends on the selectivity of the filter-feeder, the degree of infectivity by the pathogen, the mechanism(s) of pathogen transmission and the ability of the pathogen to resist degradation. For example, some bacteria and viruses can resist degradation and accumulate within a filter-feeder leading to disease transmission to humans and other wildlife upon ingestion. Since bivalves can concentrate microorganisms, they are also useful as sentinels for the presence of pathogenic microorganisms. While somewhat less studied, other invertebrates, including ascidians and sponges may also provide ecosystem services by altering pathogen transmission. In all scenarios, climate change may affect the potential for filter-feeders to mitigate disease risk. We conclude that an assessment including empirical data and modeling of system-wide impacts should be conducted before selection of filter-feeders to mitigate disease. Such studies should consider physiology of the host and microbe and risk factors for negative impacts including augmentation of other pathogens.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 it