Why we should develop guidelines and quantitative standards for using genetic data to delimit subspecies for data‐poor organisms like cetaceans
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Obtaining the representative morphological data required for traditional taxonomy is difficult, and sometimes impossible, for cetaceans, especially large ones. As a result, three quarters of the 88 currently recognized extant species have no subspecies and 40 taxa likely have additional unnamed taxa. Conservation needs give urgency to improving taxonomy because unnamed taxa are unlikely to receive protection equivalent to that received by named taxa. Genetic data can improve efforts to delimit subspecies, but the markers and methods used have varied and the magnitude of genetic difference used to justify subspecies distinctions across studies has also varied. Here, we define the concepts of populations, subspecies, and species to establish a foundation for developing guidelines (data to include and analyses to conduct) and quantitative standards (the magnitude of differentiation expected at different taxonomic levels) for using genetic data to support taxonomic recognition. Our definition is particularly applicable to data‐poor groups because it allows for naming a subspecies when there is uncertainty about whether lineages have diverged sufficiently for species‐level recognition. This allows a species that lacks convincing data for lineage divergence to be recognized as a subspecies while sufficient data are accrued, which could take decades for some cetaceans.
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.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.023 |
| 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 it