Validating taxonomic identifications in entomological research
Bibliographic record
Abstract
Abstract We surveyed the treatment of taxonomic information in 567 papers published in nine entomological journals in 2016. The proportion of papers that provide taxonomic data in sufficient detail to permit precise validation of taxonomic identifications is vanishingly small: most did not cite identification methods, most did not state whether identified material had been vouchered, and taxon concepts were almost universally absent in non‐taxonomic papers. Overall, the combination of all three factors was provided less than 2% of the time and almost two‐thirds of all papers provided none of the three. We suggest that journals should modify the templates used by editors and reviewers by overtly including the following questions: Are Order and Family named in the title, abstract or keywords? Are the methods used for identification of all studied taxa stated clearly? Is it clear who did the identifications, are they named and is their contact information and/or institutional affiliation provided? Is the literature whereupon these identifications are based cited appropriately? This would include some reference to as thorough a revisional taxon concept statement as possible, preferably from recent revisions if available. Are exemplars of all focal species (or all sampled individuals) vouchered in a named repository (ideally with contact person name and accession numbers or other means of ready detection)? Accurate and replicable taxonomic identification is the cornerstone of biology, without which entomological research risks becoming irreproducible and thus not scientific.
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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 | 0.416 | 0.768 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.075 | 0.054 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.022 | 0.022 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".