Response to Kozlov <i>et al.</i> : Inaccurate estimation of biases in herbarium specimen data
Bibliographic record
Abstract
Summary Kozlov and colleagues 1 call into question the application of herbarium specimens to quantify historical patterns of herbivory 2–5 . It is already widely appreciated that collectors of herbarium specimens may tend to avoid insect damage, thus making herbivory estimates from herbarium specimens potentially down-biased 2 . However, Kozlov et al. additionally suggest that variation in sampling selectivity among collectors and curators may lead herbarium specimens to misrepresent patterns of herbivory in nature. The authors sought to quantify these biases by collecting and contrasting insect herbivory data across 17 plant species from herbarium versus standard field ecological sampling procedures, and then assessed the selection of these specimens by curators. They concluded that herbivory estimates from herbarium specimens are highly variable, rendering them an inaccurate representation of herbivory in nature. Our re-analysis of Kozlov et al. ’s data suggests that, in contrast with their results, herbarium specimens indeed provide a useful record of herbivory as long as sample sizes are appropriate. In addition, we assert that by arguing that herbarium specimens are “distorting mirrors”, Kozlov et al. ’s conclusions fundamentally overstep their data, which narrowly assesses biases across species. Kozlov et al. argue that herbarium specimens are inaccurate data sources, but fail to characterize the specific circumstances under which assumed biases would apply. Thus, Kozlov et al. ’s data do not support their main premise, and the authors extrapolate beyond the specific biases investigated in their study; we believe their contribution does a disservice to researchers interested in exploring the potential value of herbarium specimens for studying herbivory through time.
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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 | 0.026 | 0.154 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.028 | 0.026 |
| Insufficient payload (model declined to judge) | 0.010 | 0.009 |
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".