The weight share method in forest inventories: refining the relation between points and trees
Bibliographic record
Abstract
Since it is impossible in practice to create a sampling frame for the population of forest trees, forest inventories have relied on indirect sampling methods. This indirect sampling uses two populations: the discrete populations of trees and the continuous population of points, from which trees are being sampled. Important works such as Mandallaz, Eriksson, and Stevens and Urquhart brought the fundamental elements in the formalization of the sampling of trees, by defining the duality principle that relates both populations. They led to the so-called continuous population approach where trees attributes are transformed into attribute density values. However, in these approaches, the trees quickly fade away despite being the target population, while their weight is calculated as the inverse of their inclusion probability. We explain how the generalized weight share method (GWSM) can be used to formalize the link between the two populations. GWSM allows to revisit previous concepts proposed to solve the question of how to produce estimations from tree-level attributes, under uniform random or more complex sampling designs. The principles of the method are explained, and its functioning is illustrated under a variety of points and trees sampling designs, including fixed-area, Bitterlich, and cluster sampling.
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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 | 0.017 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".