Design-based regression estimation of net change for forest inventories
Bibliographic record
Abstract
A simple design-based approach to estimating net change of a forest attribute such as timber volume is to observe the change directly on the plot level and then make that the response variable of interest using established estimation techniques such as multiphase regression estimators. This direct approach is only possible for inventories with permanent plots and is constrained to estimating net change over time periods matching the duration of the remeasurement cycle. Indirect estimation involves applying one of the aforementioned techniques to estimate the state at two time points and taking their difference. Indirect methods, although less common, are not necessarily constrained to permanent plots and can estimate net change over any desired time span for annual designs. This article compares design-based direct and indirect regression estimators under the Monte Carlo approach and illustrates their performances with data from the Swiss National Forest Inventory. The major finding is that direct estimation should be preferred whenever change is observable directly on the plot level but that multiphase indirect estimation can still improve precision when direct estimation is not possible such as for inventories employing only temporary plots.
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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 | 0.016 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".