Stratified space angle-count sampling for an estimation of stand volume
Bibliographic record
Abstract
Stratified space angle-count sampling is a newly proposed method for estimating stand volume. This new method includes the theory and all the premises of space point sampling, as well as the following: (i) all trees taper, (ii) the box-like sampling space imagined in space point sampling is divided into several strata with the same vertical distances, (iii) the diameter of expanded tree stems in each stratum is represented at the middle of the vertical distance in each stratum. Stand volume is calculated using the following equation: V (m3/ha)=kH/Z Σ[Formula: see text] λi, where V is volume (m3/ha), k is basal area factor (m2/ha), H is the maximum tree height in the stand, Z is the number of strata in the sampling space, N is the number of trees in the stand, and λi is an indicator variable that takes the value 1 or 0, depending on whether the tree stem is in the sample or not. Since this method does not require a measurement or an estimation of a critical height, it could be an easy method for estimating stand volume.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".