A New Method for Forest Volume Measurement with an Electronic Angle Gauge
Bibliographic record
Abstract
To realise precise, real-time measurement of forest volume, this study discusses the principles and method of forest volume measurement when utilizing an electronic gauge. Use of the electronic gauge, to count trees within five concentric circles, resulted in a decrease in the variation of estimated forest volume, as the number of circles increased. This estimate became reliable upon reaching the fourth concentric circle. In contrast, the use of a conventional angle gauge revealed no obvious regularity and no significant trend using multiple observation points. As well, for forest inventory plots with uneven spatial distribution, there was relatively low precision when using multiple observation points with a conventional angle gauge: the relative errors of forest volume measurement reached almost 40% in the first plot using multiple observation points. The electronic angle gauge is comprised of a telescope and Charge Coupled Device (CCD) system, which reduced the probability of a misreading and achieved accurate real-time measurement of forest volume. Observers can choose an arbitrary location to position the electronic angle gauge. The survey time with the new method was half that of using a conventional angle gauge at five different observation locations. The forest volume measurement was automated using a Personal Digital Assistant (PDA) with newly-designed software capable of identifying, registering and then counting the trees in a plot. This method improves accuracy of forest volume measurement and reduces the time and effort required previously.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".