Non-repeatable science: assessing the frequency of voucher specimen deposition reveals that most arthropod research cannot be verified
Bibliographic record
Abstract
Scientific findings need to be verifiable and grounded in repeatability. With specimen-level research this is in part achieved with the deposition of voucher specimens. These are labeled, curated, data-based specimens that have been deposited in a collection or museum, available for verification of the work and to ensure researchers are calling the same taxa by the same names. Voucher specimens themselves are the subject of research, from the discovery of new species by taxonomists to ecologists documenting historical records of invasive species. Our objective was to quantify the frequency of voucher specimen deposition in biodiversity and community ecology research through a survey of the peer-reviewed literature about arthropods, from 1989 until 2014. Overall rates of voucher deposition were alarmingly low, at under 25%. This rate increased significantly over time, with 35% of papers reporting on vouchers in 2014. Relative to the global mean, entomological research had a significantly higher rate of voucher deposition (46%), whereas researchers studying crustaceans deposited vouchers less than 6% of the time, significantly less than the mean. Researchers working in museums had a significantly higher frequency of voucher deposition. Our results suggest a significant culture shift about the process of vouchering specimens is required. There must be more education and mentoring about voucher specimens within laboratories and across different fields of study. Principal investigators and granting agencies need a proactive approach to ensuring specimen-level data are properly, long-term curated. Editorial boards and journals can also adopt policies to ensure papers are published only if explicit statements about the deposition of voucher specimens is provided. Although the gap is significant, achieving a higher rate of voucher specimen deposition is a worthy goal to ensure all research efforts are preserved for future generations.
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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 | 0.178 | 0.442 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".