A Monte Carlo appraisal of tree abundance and stand basal area estimation in forest inventories based on terrestrial laser scanning
Bibliographic record
Abstract
Non-detection of trees is an important issue when using single-scan TLS in forest inventories. A hybrid inference approach is adopted. Quoting from distance sampling, a detection function is assumed, so that the inclusion probability of each tree included within each plot can be determined. A simulation study is performed to compare the TLS-based estimators corrected and uncorrected for non-detection with the Horvitz–Thompson estimator based on conventional plot sampling, in which all the trees within plots are recorded. Results show that single-scan TLS provides more efficient estimators with respect to those provided by the conventional plot sampling in the case of low-density forests when no distance sampling correction is performed. In low-density forests, uncorrected estimators lead to a small bias (1%–6%), increasing with plot size. Therefore, care must be taken in enlarging the plot radius too much. The bias increases in forests with clustered spatial structures and in dense forests, where the bias levels (30%–50%) deteriorate the performance of uncorrected estimators. Even if the bias-corrected estimators prove to be effective in reducing the bias (below 15%), these reductions are not sufficient to outperform conventional plot sampling. Therefore, there is no convenience in using TLS-based estimation in high-density forests.
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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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".