A Trunk Diameter Estimation Mobile App for the Masses
Bibliographic record
Abstract
Forest inventories can monitor changes in forest carbon sinks and assess forest biodiversity, which is crucial for studying the role of forests in responding to climate change. In recent years, methods for implementing parts of forest surveys on smartphones have often only been supported on specialized devices or a limited number of models. Most methods also assume a large spacing between trees and unoccluded trunks, and hence are unable to cope with realistic forest environments. We present an intuitive mobile application that can run on most common Android devices and estimate tree diameter in near real-time from a single image capture. We collected 154 samples in three countries across varying latitudes, testing our app in challenging conditions including occlusion, leaning trees, and irregular shapes. Our algorithm has a MAE of 2.31 cm and an RMSE of 3.16 cm. In addition to ensuring measurement accuracy, diameter measurement using our app is approximately 5 times faster than traditional manual surveying. Our research offers a low-cost, accurate solution for rapid trunk diameter measurement, expanding its applicability to a wider range of smartphone models and field scenarios.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.026 | 0.012 |
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".