The Amphibian Genomics Consortium: advancing genomic and genetic resources for amphibian research and conservation
Bibliographic record
Abstract
Amphibians represent a diverse group of tetrapods, marked by deep divergence times between their three systematic orders and families. Studying amphibian biology through the genomics lens increases our understanding of the features of this animal class and that of other terrestrial vertebrates. The need for amphibian genomic resources is more urgent than ever due to the increasing threats to this group. Amphibians are one of the most imperiled taxonomic groups, with approximately 41% of species threatened with extinction due to habitat loss, changes in land use patterns, disease, climate change, and their synergistic effects. Amphibian genomic resources have provided a better understanding of ontogenetic diversity, tissue regeneration, diverse life history and reproductive modes, anti-predator strategies, and resilience and adaptive responses. They also serve as essential models for studying broad genomic traits, such as evolutionary genome expansions and contractions, as they exhibit the widest range of genome sizes among all animal taxa and possess multiple mechanisms of genetic sex determination. Despite these features, genome sequencing of amphibians has significantly lagged behind that of other vertebrates, primarily due to the challenges of assembling their large, repeat-rich genomes and the relative lack of societal support. The emergence of long-read sequencing technologies, combined with advanced molecular and computational techniques that improve scaffolding and reduce computational workloads, is now making it possible to address some of these challenges. To promote and accelerate the production and use of amphibian genomics research through international coordination and collaboration, we launched the Amphibian Genomics Consortium (AGC, https://mvs.unimelb.edu.au/amphibian-genomics-consortium ) in early 2023. This burgeoning community already has more than 282 members from 41 countries. The AGC aims to leverage the diverse capabilities of its members to advance genomic resources for amphibians and bridge the implementation gap between biologists, bioinformaticians, and conservation practitioners. Here we evaluate the state of the field of amphibian genomics, highlight previous studies, present challenges to overcome, and call on the research and conservation communities to unite as part of the AGC to enable amphibian genomics research to "leap" to the next level.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".