Estimating Stem Diameter Distributions in a Management Context for a Tolerant Hardwood Forest Using ALS Height and Intensity Data
Bibliographic record
Abstract
Two types of nonparametric modeling techniques and various metrics derived from airborne laser scanning (ALS) data were examined in terms of their utility for modeling stem diameter distributions in an uneven-aged tolerant hardwood forest in Ontario, Canada. Using an area-based approach (ABA), the frequency distribution of trees across 6 size classes was predicted using k-nearest neighbor (k-NN) imputation and Random Forest (RF) regression. Predictor variables derived from ALS height and intensity data were divided into 3 groups: height only, intensity only, and all metrics. Prediction results demonstrated that the first 2 groups of predictor variables exhibited similar predictive accuracy, whereas the synergy of both resulted in enhanced performance. The utility of intensity-based metrics was corroborated by an importance measure obtained from RF. The size class-specific stem density estimation approach based on RF was more accurate and flexible than the simultaneous estimation approach based on k-NN models. After the predicted diameter distributions were grouped into 9 structural groups, heterogeneous accuracy scores revealed the challenges for predicting select diameter distributions. Although successes were observed for certain size classes, there remains additional research (e.g., development of additional metrics or data types) to be done to accurately predict a complete range of size classes.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".