Acadian Forest Volume Estimates Derived from Airborne LiDAR, Big BAF Sample Plots, and Fixed Area Plots on the Noonan Research Forest
Bibliographic record
Abstract
The data are located on a 100 m N-S/E-W grid on the 80-ha Femelschlag Research Site on the Noonan Research Forest in New Brunswick, Canada. “Femel_EFI_GTV” contains the gross total volume (GTV; m3ha-1) estimates derived from airborne LiDAR scanning as part of the government of New Brunswick’s Enhanced Forest Inventory program. “Noonan_FemelPlot_GTV” contains the GTV estimates using four different plot types: fixed area plots with all trees measured for height; fixed area plots with 3P subsampling of heights; horizontal point sampling with all trees measured; and big BAF sample plots. The fixed area plots were circular with an 11.28 m radius and all tree 6cm DBH and greater were identified by species and measured for DBH and height. The 3P subsampling was simulated using a Height – Diameter curve as prediction and a target subsample size of 100 trees across the 83 plots. The horizontal point samples used a 2-M BAF angle gauge to select count trees for measurement. All trees 6 cm DBH and greater and considered “in” were identified by species and DBH measured. Heights were imputed using a Height – Diameter curve and the heights measured on the big BAF plots. The big BAF plots had the same plot design as the horizontal point samples and a 27-M BAF angle gauge was used to select trees for height measurement. All four plot types were center of the 100 m grid intersections. Grids do not align with EFI cells because the 100 m grid was established independently and prior to the EFI cells. The X – Y variables are the locations (in m) of the cell center (for Femel_EFI_GTV) and the plot centers (for Noonan_FemelPlot_GTV) with {0,0} being the SW corner of the study area.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".