Bibliographic record
Abstract
Amphibians are important vertebrates in toxicology often representing both aquatic and terrestrial forms within the life history of the same species. Of the thousands of species, only two have substantial genomics resources: the recently published genome of the Pipid, Xenopus (Silurana) tropicalis, and transcript information (and ongoing genome sequencing project) of Xenopus laevis. However, many more species representative of regional ecological niches and life strategies are used in toxicology worldwide. Since Xenopus species diverged from the most populous frog family, the Ranidae, ~200 million years ago, there are notable differences between them and the even more distant Caudates (salamanders) and Caecilians. These differences include genome size, gene composition, and extent of polyploidization. Application of toxicogenomics to amphibians requires the mobilization of resources and expertise to develop de novo sequence assemblies and analysis strategies for a broader range of amphibian species. The present mini-review will present the advances in toxicogenomics as pertains to amphibians with particular emphasis upon the development and use of genomic techniques (inclusive of transcriptomics, proteomics, and metabolomics) and the challenges inherent therein.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".