Branch aggregation and crown allometry condition the precision of randomized branch sampling estimators of conifer crown mass
Bibliographic record
Abstract
Characterizations of conifer crown biomass are important for assessing forest fuel loadings, bioenergy supplies, carbon stocks, and growth and yield. There is considerable variation in conifer crown mass, but to guide sampling programs, there is little quantitative information available concerning its structure or extent. This research examines several randomized branch sampling (RBS) strategies adapted for excurrent crown forms, as well as the impact of allometric relationships on their precision. The RBS strategies differ in terms of how primary branches (those attached directly to the bole) are aggregated into selection nodes and are evaluated using destructively sampled Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) and western larch (Larix occidentalis Nutt.) trees. Strong linear relationships between branch mass and basal area improve the precision of RBS relative to simple random sampling. Yet because the sample trees exhibited area-increasing branching patterns, the RBS strategies could not achieve the same precision as two-pass probability proportional-to-size methods. For the same reason, aggregation of branches improved the precision of RBS. For practical reasons, we recommend aggregating branches by 1 m intervals along the stem. This strategy brought the relative standard errors for crown mass below 20% with sample sizes of four to eight branches, with smaller sample sizes yielding this result for more slender trees. We also report results on the variability of branch area and mass, as well as results that recommend against the practice of allocating equal numbers of sample branches to crown strata of equal length.
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 | 0.017 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".