Biological species and taxonomic species: Will a new null hypothesis help? (A comment on Gill 2014)
Bibliographic record
Abstract
In his article “Species taxonomy of birds: Which null hypothesis?”, Gill (2014) recommended that we might apply our growing knowledge of avian speciation more effectively, particularly to avian taxonomy and the definition of species. Specifically, Gill (2014) suggested that committees on avian nomenclature should operate under a new null hypothesis for species designation: Genetically and phenotypically distinct taxa would be considered full species, a priori, in the absence of any natural tests of reproductive isolation (i.e. sympatric populations) or additional evidence of possible isolating barriers. There are several useful aspects to this suggestion. However, in this Commentary, I present a number of issues that suggest that such a proposal may be premature. More generally, I recommend that unless a more compelling argument is made for altering the status quo, it seems prudent for nomenclature committees to continue to use the best available evidence to make informed decisions about the extent of reproductive isolation between putative avian species.
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 | 0.040 | 0.110 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.028 |
| Scholarly communication | 0.007 | 0.016 |
| Open science | 0.012 | 0.008 |
| Research integrity | 0.087 | 0.113 |
| Insufficient payload (model declined to judge) | 0.007 | 0.009 |
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".