Transcriptomics for understanding marine fish larval development<sup>1</sup>This review is part of a virtual symposium on current topics in aquaculture of marine fish and shellfish.
Bibliographic record
Abstract
The larval phase is a crucial period in the life of marine fish. During this phase, the organism will acquire the phenotype of an adult fish through the development of tissues and organs and the maturation of some of the principal physiological functions. Many biological processes (differentiation, cellular proliferation, growth, etc.) are regulated during this period. These regulations take place at different biological levels and particularly concern the expression of genes involved in larval ontogenesis processes. The development of bioinformatic resources (DNA or cDNA sequences) and molecular tools enabling high throughput gene expression analysis (microarrays) have allowed the transcriptome of marine fish species to be studied. In the present review, we summarize the main findings from transcriptomic investigations of development of marine fish larvae. Special attention is paid to investigations of transcriptomic patterns during postembryonic development and to the impact of environmental or nutritional factors on the transcriptome of marine fish larvae. Transcriptomic approaches will be especially useful in the future for investigating the effect of temperature and water acidification (or pH) on the development of different fish species in the context of global climate change.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".