Inadequate treatment of taxonomic information prevents replicability of most zoological research
Bibliographic record
Abstract
We evaluated the quality of information about taxonomic identifications in 710 papers published in seven zoological journals in 2017. We found that only 10.7% of papers cited identification methods, 29.2% made available specimen-level material for later verification, and 6.9% indicated taxon concepts applied to studied animals. Only 4.0% provided details about all three practices, while almost two-thirds provided none. Invertebrate papers were more likely than vertebrate papers to provide identification methods and deposit vouchers, but taxon concepts were rarely provided, and none of the three practices were common in any category. In short, our data suggest that most zoological research is irreplicable. To address this problem, journals should require submitted manuscripts to meet the following guidelines: (1) methods used to identify studied taxa must be stated; (2) literature supporting these identifications must be cited; (3) taxon concept(s) applied to species-level taxa must be indicated; (4) specimen-level material should be available for later examination. We argue that research which falls short of these guidelines is not replicable. We provide recommendations for how authors can better document how studied animals are identified and permit others to verify their identifications, which is necessary for transparent, replicable, and ultimately scientific zoological research.
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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 | 0.503 | 0.802 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.039 | 0.036 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".