Bibliographic record
Abstract
Phylogenetic analysis forms an important component of research in the life sciences, enabling the study of evolutionary relationships, timescales, patterns, and processes. Phylogenetic trees are used in a wide variety of research fields, including molecular ecology, taxonomy and systematics, conservation genetics, and microbiology. Accordingly, there is a strong demand for accessible training in phylogenetic analysis, especially among postgraduate students and other early career researchers. Although there are several regular workshops that provide opportunities for training in phylogenetics, these are predominantly held in Europe and North America, posing logistical and financial barriers for prospective participants in the Southern Hemisphere and Asia-Pacific region. The Sydney Phylogenetics Workshop has aimed to address this gap by offering an annual training opportunity suitable for early career researchers. The workshop blends theory with practical exercises using industry-standard software, while its content has evolved over its 15-year history to meet the changing needs of attendees. In this article, we provide an outline of the content and format of the workshop, the challenges associated with running such an event on a minimal budget, and the measures taken to improve accessibility and reduce environmental impact. We hope that the workshop provides a useful model for organizing effective training opportunities for early career researchers in the life sciences.
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 | 0.017 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.025 | 0.013 |
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".