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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.104 |
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; both teacher heads agree on what is shown here.
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".