Measurement and prediction of bark thickness in <i>Picea abies</i>: assessment of accuracy, precision, and sample size requirements
Bibliographic record
Abstract
Tree and log diameters are usually measured outside bark, but inside-bark diameters are of greater economic interest and are often derived with local or regional bark thickness equations. To date, the influence of measurement method, sampling design, and sample size on bark thickness equation accuracy and precision has received limited attention. The objectives of this study were to use an extensive regional bark thickness dataset for Norway spruce (Picea abies (L.) Karst) in southwestern Germany to (1) quantify the accuracy and precision of bark thickness measurements with a Swedish bark gauge, (2) determine the required number of measurements to assess the within-tree variation, and (3) estimate the required sample sizes per plot and per region to develop an accurate bark thickness prediction equation. Bark gauge readings were validated with measurements derived from X-ray computed tomography (CT) and indicate that Swedish bark gauges generally overestimated bark thickness by 13.6% ± 28.4% (mean ± standard deviation). Results suggested having at least one measurement location every 2 m along a tree bole and at least five bark thickness measurements per each of these locations to achieve an allowable error of <15%. For the study area, Monte Carlo simulations indicated that a total sample size of 50–250 trees was needed, depending on the complexity of the desired bark thickness model. Overall, this analysis indicated that there was relatively high within- and between-tree variation in bark thickness, but adequate sampling methods and sample sizes produced highly accurate bark thickness equations.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".