Evaluation of Digital Photography for Quantifying <I>Cryptococcus fagisuga</I> (Hemiptera: Eriococcidae) Density on American Beech Trees
Bibliographic record
Abstract
Beech scale (Cryptococcus fagisuga Lindinger) (Hemiptera: Eriococcidae) is an invasive forest insect established in the eastern United States and Canada. It predisposes American beech (Fagus grandifolia Ehrhart) trees to infection by Neonectria spp. Fungi causing beech bark disease. White wax secreted by the diminutive scales obscures individual insects, making it difficult to accurately quantify beech scale density. Our goals were to 1) evaluate the relationship between the area of wax and number of beech scales on bark samples, 2) determine whether digital photos of bark could accurately quantify beech scale density, and 3) compare efficiency and utility of a qualitative visual estimate and using the quantitative digital photo technique to assess beech scale populations. We visually estimated beech scale abundance and photographed designated areas on the trunk of 427 trees in 40 sites across Michigan. Photos were analyzed using a binary threshold technique to quantify the area of beech scale wax on each photo. We also photographed and then collected 104 bark samples from 45 additional beech trees in ten sites. We removed the wax, counted individual scales on each sample using a microscope, and assessed the linear relationship between wax area and scale counts. Area of wax explained approximately 80% of the variability in scale density. We could typically quantify beech scale density on 15 photographs per hour. Qualitative visual assessments of beech scale in the field corresponded with estimates derived from photos of bark samples for 79% of trees.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".