Blood-based gene expression as non-lethal tool for inferring salinity-habitat history of European eel (Anguilla anguilla)
Bibliographic record
Abstract
The European eel is a facultative catadromous species, meaning that it can skip the freshwater phase or move between marine and freshwater habitats during its continental life stage. Otolith microchemistry, used to determine the habitat use of eel or its salinity history, requires the sacrifice of animals. In this context, blood-based gene expression may represent a non-lethal alternative. In this work, we tested the ability of blood transcriptional profiling to identify the different salinity-habitat histories of European eel. Eels collected from different locations in Norway were classified through otolith microchemistry as freshwater residents (FWR), seawater residents (SWR) or inter-habitat shifters (IHS). We detected 3451 differentially expressed genes from blood by comparing FWR and SWR groups, and then used that subset of genes in a machine learning approach (i.e., random forest) to the extended FWR, SWR, and IHS group. Random forest correctly classified 100% of FWR and SWR and 83% of the IHS using a minimum of 30 genes. The implementation of this non-lethal approach may replace otolith-based microchemistry analysis for the general assessment of life-history tactics in European eels. Overall, this approach is promising for the replacement or reduction of other lethal analyses in determining certain fish traits.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".