Biomass Data from Sector Subsampling of 360˚ Spherical Images
Bibliographic record
Abstract
The base data were collected from 3 early spacing trials located in western Newfoundland, Canada (Cormack, Pasadena, Roddickton) and established in the early 1980s (Donnelly et al. 1986; Fig.1). All trees 1.3 m and taller were identified by species and measured for diameter at breast height and total height periodically since the time of treatment. Using the most recent measurement at each trial, stemwood, bark, branch, and foliage biomass (kg) was estimated for each tree using the Canadian national biomass equations. Total above-ground biomass was estimated by summing these components. Total basal area was estimated by calculating tree cross-sectional area, multiplying by the appropriate plot expansion factor, and summing across all live trees. Spherical photos were obtained at three photo sample points located at half the plot radius and at azimuths of 0˚, 120˚, and 240˚. Spherical photos were obtained a 1.6m and 2.6m above the ground. Photo horizontal point sampling (Wang et al. 2020) using a 2 M photo basal angle gauge was used to estimate basal area at each sample point using the 1.6m spherical photo. Sector subsampling was then used to select measure trees using a randomly generated sector azimuth and a sector angle of 7.2˚ (2% of 360˚). All visible trees within each sector were then measured for total height and diameter at breast height using the spherical stereographic techniques. Biomass was then estimated using the Canadian National Biomass Equations, assuming all sector-selected trees were balsam fir. The plot-level data and measure-tree data were used to estimate aboveground biomass (BM, tonnes∙ha-1) using simulated resampling of the photo sample point data and ratio estimation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".