On the Importance of Contrasts in Taxonomic Diagnoses: A Survey of 405 Newly Described Insect Genera
Bibliographic record
Abstract
Diagnoses are one of the most important ways that taxonomists make new taxa recognisable by others. A recent paper stated that diagnoses for newly described organisms should include both the characteristic(s) of the new taxon (providing state-specificity) and name the taxa with which they are compared (giving contrastiveness). I argue that the characteristics of the compared taxa should also be included such that the diagnostic features are not only contrastIVE but also overtly contrastED I surveyed 278 papers wherein 405 new insect genera were described. Forty-four genera did not have a formal diagnosis, among the rest, there was a total of 427 diagnoses because some genera had multiple diagnoses. Among these, 83 (19.4%) had one or more diagnostic states overtly contrasted and over one-eighth (13.9%) contrasted all of them. Unsurprisingly, diagnoses that overtly compared the new genus to others were significantly more likely to include characteristics that were contrasted than did diagnoses that were lists or combinations. I discuss how features should be contrasted in diagnoses.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".