Precision and accuracy in quantifying herbivory
Bibliographic record
Abstract
1. Tissue removal by herbivores (i.e. herbivory) is a dominant interaction in most communities which has important impacts on natural and managed ecosystems. Despite the importance of herbivory, we lack a quantitative comparison of the efficacy of the most commonly used methods used to quantify herbivore damage. 2. We examined the factors that affect the precision and accuracy of visual and digital methods commonly used to quantify damage to leaves. 3. We created 224 digital leaves from four plant species. In a fully factorial design we manipulated leaf morphology and species, the location of damage (marginal or internal), estimation method (exact percentage or 25% bins), observer experience and expectancy bias (i.e., bias due to an expected result). Using 583 adult observers, we estimated the precision and accuracy of individuals' ability to visually estimate known levels of damage. In a third smaller experiment, we performed similar analyses using a digital scanner. 4. Across the first two experiments, individuals estimated damage with high precision ( R 2 = 0.75 and 0.80) and accuracy (slope actual vs estimated = 0.88 and 0.86). However, the precision and accuracy of estimates were influenced by plant species, the location of damage, and estimation method. Inexperienced individuals also overestimated low levels of damage, and this bias decreased with experience. Digital methods were precise ( R 2 = 0.98) whereas accuracy was statistically indistinguishable from visual methods (slope = 0.91). 5. Visual estimates of damage provide the fastest and most cost‐effective method for quantifying herbivory, and our results show they can be precise and accurate. We use our results to provide specific recommendations for future 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 | 0.045 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".