High-resolution digital soil mapping for managed forests using airborne LiDAR data
Bibliographic record
Abstract
A goal of sustainable forest management using digital soil mapping (DSM) is to ensure that current and future generations have the best soil information so they can use forest resources wisely.This goal can be achieved using new technologies of generating digital soil maps and high-resolution light detection and ranging (LiDAR) data.Uncertainty in digital soil maps can be quantified using quantile regression (QR).The overall objective of this study is to generate several digital soil maps using different machine learning (ML) methods for forest management purposes and use a QR method to estimate their uncertainty.The study area is the Eagle Hill Forest (95 km 2 ), located west of Kamloops, BC, Canada.Five soil properties were mapped and locations with soil erosion, displacement, and compaction and puddling hazards were displayed on maps and discussed.90% prediction interval (PI) maps were produced and the performance of the QR method in uncertainty quantification of different ML models was illustrated by producing Prediction Interval Coverage Probability (PICP) plots.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".