Predicting forest understory habitat for Canada lynx using LIDAR data
Bibliographic record
Abstract
ABSTRACT Canada lynx ( Lynx canadensis ) is a federally threatened species in the contiguous United States. Within National Forests covered by the Northern Rockies Lynx Management Direction, Federal land managers must consider the effect of management activities on Canada lynx habitat. A common method to assess Canada lynx habitat used by the U.S. Forest Service is to measure horizontal cover using a cover board. We used field measurements and airborne Light Detection and Ranging (LIDAR) metrics to test beta regression models that predict estimates of horizontal cover on the Nez Perce–Clearwater National Forest, Idaho, USA, 2009–2015. We also investigated the effect on model predictions when the cover board was blocked by the main stem of a tree. Model fit statistics for normalized root mean square errors (RMSE%) were 30.8–33.7% and pseudo‐ R 2 ranged from 0.64 to 0.71. Using independent validation data, model performance statistics for RMSE% were 24.6–33.5% and R 2 ranged from 0.51 to 0.69. We found that removing cover board measurements where the main stem of a tree blocked >75% of the cover board produced the best model statistics. These models can be applied across LIDAR extents resulting in maps of horizontal cover estimates, which may be used in assessing effects of management activities on Canada lynx habitat. © 2019 The Wildlife Society.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 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".