Bibliographic record
Abstract
Much of what has recently been written about taxonomy has focused on negatives in the face of a heterogeneously defined taxonomic impediment. The current review takes a step back from the rhetoric to explicate the modern science of taxonomy with a new practical model, “the five ‘D’s”: taxon discovery, delimitation, diagnosis, description, and specimen determination. Although individual taxonomists may focus more on some of these practices and less on others, taxonomy as a discipline requires all five. Each practice depends on the one prior and necessarily leads to and often overlaps with the one following. In fact, the first ‘D’—taxon discovery—has its origin in the last, specimen determination, thereby closing a recursive loop of taxonomic progress. Hopefully users of taxonomy—almost all biologists—will appreciate a fresh perspective on a foundational science. Several recommendations are offered to biological researchers to account for the iterative improvement, and hence necessary change, in the taxonomy and nomenclature of their study organisms.
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 | 0.027 | 0.086 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.018 | 0.017 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.009 | 0.008 |
| Research integrity | 0.006 | 0.017 |
| Insufficient payload (model declined to judge) | 0.043 | 0.049 |
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".