Examination of uncertainty in per unit area estimates of aboveground biomass using terrestrial LiDAR and ground data
Bibliographic record
Abstract
In estimating aboveground forest biomass (AGB), three sources of error that interact and propagate include (i) measurement error, the quality of the tree-level measurement data used as inputs for the individual-tree equations; (ii) model error, the uncertainty about the equations of the individual trees; and (iii) sampling error, the uncertainty due to having obtained a probabilistic or purposive sample, rather than a census, of the trees on a given area of forest land. Monte Carlo simulations were used to examine measurement, model, and sampling errors and to compare total uncertainty between models and between a phase-based terrestrial laser scanner (TLS) and traditional forest inventory instruments. Input variables for the equations were diameter at breast height, total tree height (defined the height from the uphill side of the tree to the tree top), and height to crown base; these were extracted from the terrestrial LiDAR data. Relative contributions for measurement, model, and sampling errors were 5%, 70%, and 25%, respectively, when using TLS, and 11%, 66%, and 23%, respectively, when using the traditional inventory measurements as inputs into the models. We conclude that the use of TLS can reduce measurement errors of AGB compared with traditional inventory measurements.
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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 | 0.023 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".