An analysis of sensitivities contributing measurement error to Resistograph values
Bibliographic record
Abstract
As a hand-held, portable tool capable of assessing wood density of standing trees quickly and inexpensively, the Resistograph holds considerable potential application for evaluating forest genetics field tests. However, phenotypic correlations between Resistograph density index values and values from conventional wood density assessment techniques are weak. In an effort to investigate the extent to which environmental, operator, or instrument factors may affect density index values, we evaluated the sensitivity of Resistograph measurements to seven experimental factors. Drill bit flexion (a measure of operator steadiness), moisture content of wood, and air temperature significantly affected Resistograph density index values, while the influence of knots is minimized at a vertical distance of 3 cm. Battery type, sharpness of the drill bit (at least up to 350 uses), and battery charge (at least up to 310 uses with a 12 V motorcycle battery) had no significant effect on density index. By ensuring that the operator remains steady while drilling, sampling only live trees, only when air temperatures are above freezing, and by drilling at least 3 cm vertical distance from knots, measurement error should be minimized. Three measurements per tree are required to estimate density index to within 3 units 19 times out of 20.
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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 | 0.033 | 0.171 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".