New Species Described From Photographs: Yes? No? Sometimes? A Fierce Debate and a New Declaration of the ICZN
Bibliographic record
Abstract
The option of describing new taxa using photographs as proxies for lost or escaped (‘unpreserved’) type specimens has been rarely used but is now undergoing renewed scrutiny as taxonomists are increasingly equipped to capture descriptive information prior to capturing and preserving type specimens. We here provide a historical perspective on this practice from both nomenclatural and practical points of view, culminating in a summary and discussion of a new Declaration of the International Commission of Zoological Nomenclature containing recommendations about descriptions without preserved specimens. We conclude that although descriptions using photographs as proxy types are Code-compliant and occasionally justified, the conditions under which such descriptions are justified are likely to remain relatively rare. Increasing restrictions on specimen collecting, which we deplore because of the centrality of collecting and collections to all of biodiversity science, could lead to more ‘proxy type’ descriptions in those taxa in which photographs can provide sufficient information for descriptions, but we predict that such cases will remain infrequent exceptions.
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 | 0.022 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.012 | 0.015 |
| Insufficient payload (model declined to judge) | 0.019 | 0.010 |
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".