Can Ancient Stands of Cedar-Hemlock within Old-Growth Forests be Identified using LiDAR?
Bibliographic record
Abstract
The data contained within describe and support the finding ancient forests project. This research proposed to use light detection and ranging data to quantify 47 forest stand metrics for differentiating old-growth from ancient forests. The study area included 120 plots of 400 square meters located in interior cedar-hemlock forests surrounding Kootenay lake in southeastern British Columbia. Stratification between stands in wet and mesic sites has been hypothesized to allow for more accurate delineation. For this reason, old and ancient forest areas were separated by areas of infrequent and rare stand initiating events. The analysis found that measurable relationships exist between ancient and old-growth forest categories when stratified by wet and mesic environments; however, the relationships vary by category and age. No overarching combination of metrics explained the variation between all categories. Additionally, variation within categories far exceeded that between categories, so a regression equation could not be established. A random forest classification of the data found that the distinction could only be accurately predicted between 25 and 60 percent of the time, and different iterations of the same model exhibited extreme variation. The validity of the results was limited by reliance on estimated attributes from the Vegetation Resources Inventory. If this methodology were to be repeated with field verified measurements as input data, it may be able to mitigate these issues and provide the basis for a reliable predictive classification. This classification could provide an accurate and objective standard that would aid in the identification and conservation of ancient forests.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".