Novel Application of Non‐Invasive Methodological Approaches in Biomedical Sciences Towards Better Understanding of Marine Teleost Ocular Health and Disease
Bibliographic record
Abstract
Seafood is an important resource for global nutrition and food security, with both land and marine aquaculture playing pivotal roles. High visual acuity is key for health and survival of farmed, cultured, and wild fish. Cleaner fish technology to control parasite infestation has become important in marine aquaculture and highlights the importance of visual acuity in the efficacy of cleaner fish species. New clinical diagnostic approaches towards understanding and optimising fish visual health could benefit both aquacultured and wild fish populations. Opportunities for developing and using advanced non-invasive clinical assessment and diagnosis of ocular health in wild, cultured, and experimental fish are key to more rapidly realising how threats to eye health in these animals might be better understood and mitigated. Ophthalmoscopy can rapidly and non-invasively image anatomical aspects of retinal and anterior ocular tissues and has been used in mammalian biomedicine since the turn of the 20th century. More now than ever, labour-intensive post-mortem approaches for ocular analysis such as histology are increasingly being replaced or supplemented by application of various forms of optical coherence tomography (OCT) imaging of ocular tissues in mammalian biomedicine. Advances and availability of other methodological approaches such as three-dimensional printing and computer science make instrument customisation affordable and adaptable. This review article will outline how ophthalmoscopy, OCT, and other methodologies are being applied towards understanding ocular health in teleost fish species and will describe some of the future opportunities that technological advances might afford in advancing ocular imaging in fish health and disease in general.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| 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".