A survey of keys for the identification of newly described insect genera: recommendations for authors, reviewers, editors, and publishers
Bibliographic record
Abstract
Large numbers of new taxa are described annually and while there is a great need to make them identifiable, there seems little consistency in how this might be facilitated. 427 papers published in 2021 and 2022 were surveyed, which described 587 new insect genera. Only 136 of these papers included keys, and these allowed the identification of 233 of the new genera (31.9% of papers and 39.7% of the new genera). The proportion of papers that included a key varied significantly among insect orders but not among the handful of journals wherein the bulk of the new genera were described. Overall, for 17 key-related variables assessed in a binary fashion (optimal vs suboptimal), the average key had almost six criteria that were scored as being suboptimal. For example, less than one-fifth facilitated retracing and less than 12% had illustrated keys where the images were conveniently located close to the relevant key couplets. Progress towards confirming a putative identification was possible in all papers, through the inclusion of a diagnosis, habitus images, or both. Based upon this analysis, and expanding on previous suggestions for key construction, 23 recommendations are made on how to make an identification key maximally useful for users and I indicate the relative ease with which each could be adhered to. Identification keys should accompany all new taxon descriptions, guidelines for effective key construction should be added to journals' instructions to authors, editors and reviewers should check keys carefully, and publishers should be attentive to the needs of users through, for example, permitting duplication of images to make keys easier to use. Recommendations are likely relevant to all levels in the taxonomic hierarchy for all organisms, despite the data being derived from generic-level keys for insects.
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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 | 0.168 | 0.355 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.033 | 0.033 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.014 | 0.024 |
| Open science | 0.009 | 0.005 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.020 |
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".