Modelling temporal change in inventory attributes from a LiDAR-derived inventory for the United Counties of Prescott and Russell, Ontario: A comparison of random forest and linear regression methods
Bibliographic record
Abstract
This study assessed the feasibility of updating a forest inventory derived from 2014 Light Detection and Ranging (LiDAR) data using ground plot data collected in 2021 to model change in basal area, volume, and average stand height. These attributes were determined for a subset (n=32) of stands from the original 2014 inventory. Both 2nd order polynomial regression and random forest learning methods were used to model annual growth increments for these attributes and results were compared. Except for height, the variance explained using random forest regression was greater than that explained using linear regression. As well, root mean square error was lower using random forest as opposed to linear regression for all three attributes, suggesting random forest produced more accurate results overall. Although the random forest results could not be extrapolated to the landscape with confidence due to limitations associated with that approach. Rather, the quadratic equations from the linear regression models were used to predict 2021 landscape values. The results at the landscape scale were deemed to be reasonable in terms of ecological expectations despite recognized model weaknesses. Increasing sample size to capture a greater diversity of stand types and allow for species-specific modeling would no doubt result in much better predictions.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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 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".