Evaluation of branch sampling, ocular assessments, and aerial surveys for estimating spruce budworm defoliation
Bibliographic record
Abstract
We compared three methods for estimating current-year spruce budworm ( Choristoneura fumiferana (Clem.)) defoliation from 2014 to 2021 using a network of 99 permanent sample plots in central Gaspé Peninsula, Québec. Percent current-year defoliation was measured by assessing shoots from mid-crown branches, ocular ratings of all individual trees using binoculars, and provincial government aerial surveys. Ocular survey defoliation differed from branch sample defoliation in 5–6 out of 7 years, consistently underestimating defoliation, across the full range of defoliation severity observed. Nested mixed-effect models for fir–spruce combined, balsam fir, white spruce, and black spruce ocular survey defoliation bias resulted in marginal R2 of 0.40, 0.47, 0.82, and 0.86, respectively. Current defoliation severity and its interaction with previous year defoliation and weather conditions significantly affected ocular survey bias. Correspondence of aerial survey estimates and mean plot defoliation occurred in only 43% of all plot-years and ranged from 14%–58% in individual years. Differences between aerial survey defoliation and plot values mainly resulted from assigning an adjacent class (e.g., light <30% assigned as moderate 31%–70% defoliation) or misplaced defoliation polygon boundaries, suggesting that assignment of aerial survey defoliation to plots or specific ground areas needs ground truth sampling.
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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 | 0.019 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".