Opportunities and challenges with transitioning to non‐lethal sampling of wild fish for microbiome research
Bibliographic record
Abstract
The microbial communities of fish are considered an integral part of maintaining the overall health and fitness of their host. Research has shown that resident microbes reside on various mucosal surfaces, such as the gills, skin, and gastrointestinal tract, and play a key role in various host functions, including digestion, immunity, and disease resistance. A second, more transient group of microbes reside in the digesta, or feces, and are primarily influenced by environmental factors such as the host diet. The vast majority of fish microbiome research currently uses lethal sampling to analyse any one of these mucosal and/or digesta microbial communities. The present paper discusses the various opportunities that non-lethal microbiome sampling offers, as well as some inherent challenges, with the ultimate goal of creating a sound argument for future researchers to transition to non-lethal sampling of wild fish in microbiome research. Doing so will reduce animal welfare and population impacts on fish while creating novel opportunities to link host microbial communities to an individual's behavior and survival across space and time (e.g., life-stages, seasons). Current lethal sampling efforts constrain our ability to understand the mechanistic ecological consequences of variation in microbiome communities in the wild. Transitioning to non-lethal sampling will open new frontiers in ecological and microbial research.
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 | 0.111 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".