A design-based assessment of an expanded set of auxiliary information for forest growth estimation
Bibliographic record
Abstract
To improve the design-based precision of gross increment estimates from forest inventories, we propose an assessment of an expanded set of auxiliary information grouped from five sources: (i) a vegetation height model, (ii) satellite imagery, (iii) spatial data, (iv) topography, and (v) variables identified by external forest monitoring and research networks. The former two are from optical remote sensing and the latter three are chosen on the basis of interpretable and proven connections with forest growth. We evaluate each source individually and collectively for the Swiss National Forest Inventory using two-phase estimation with the elastic net method. In terms of relative efficiency, all individual groups demonstrated improvement over one-phase estimation by 7% to 29% with the interpretable sources consistently outperforming those based on optical remote sensing. However, the interpretable sources do not provide significant additional gains when combined together, whereas the optical remote sensing consistently and substantially supports other sources of auxiliary data when combined, leading to a 50% to 71% improvement overall. Given the availability of data, such as that from international monitoring programs, expanding the set of auxiliaries to include both interpretable sources and optical remote sensing is a feasible and promising option for national forest inventories.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".