Bibliographic record
Abstract
The past two decades have demonstrated a great potential for airborne Light Detection and Ranging (LiDAR) data to improve the efficiency of forest resource inventories (FRIs). In order to make efficient use of LiDAR data in FRIs, the data need to be related to observations taken in the field. Various modeling techniques are available that enable a data analyst to establish a link between the two data sources. While the choice for a modeling technique may have negligible effects on point estimates, different model techniques may deliver different estimates of precision. This study investigated the impact of various model and variable selection procedures on estimates of precision. The focus was on LiDAR applications in FRIs. The procedures considered included stepwise variable selection procedures such as the Akaike Information Criterion (AIC), the corrected Akaike Information Criterion (AICc), and the Bayesian (or Schwarz) Information Criterion. Variables have also been selected based on the condition number of the matrix of covariates (i.e., LiDAR metrics) and the variance inflation factor. Other modeling techniques considered in this study were ridge regression, the least absolute shrinkage and selection operator (Lasso), partial least squares regression, and the random forest algorithm. Stepwise variable selection procedures have been considered in both, the (design-based) model-assisted, as well as in the model-based (or model-dependent) inference framework. All other techniques were investigated only for the model-assisted approach. In a comprehensive simulation study, the effects of the different modeling techniques on the precision of population parameter estimates (mean aboveground biomass per hectare) were investigated. Five different datasets were used. Three artificial datasets were simulated; two further datasets were based on FRI data from Canada and Norway. Canonical vine copulas were employed to create synthetic populations from the FRI data. From all populations simple random samples of different size were repeatedly drawn and the mean and variance of the mean were estimated for each sample. While for the model-based approach only a single variance estimator was investigated, for the model-assisted approach three alternative estimators were examined. The results of the simulation studies suggest that blind application of stepwise variable selection procedures lead to overly optimistic estimates of precision in LiDAR-assisted FRIs. The effects were severe for small sample sizes (n = 40 and n = 50). For large samples (n = 400) overestimation of precision was negligible. Good performance in terms of empirical standard errors and coverage rates were obtained for ridge regression, Lasso, and the random forest algorithm. This study concludes that the use of the latter three modeling techniques may prove useful in future LiDAR-assisted FRIs.
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".