Data assimilation in stand-level forest inventories
Bibliographic record
Abstract
The development of remote sensing methods through research and large-scale application nowadays makes it possible to obtain stand-level estimates of forest variables at short intervals and at low cost. This offers substantial possibilities to forestry practitioners, but it also poses challenges regarding how cost-efficient data acquisition strategies should be developed. For example, should cheap but low-quality data be acquired and discarded whenever new data become available or should investments be made in high-quality data that are continuously updated to last over a longer period of time? We suggest that the solution could be to establish data assimilation (DA) procedures linked to forest inventories to make appropriate use of data from several sources. With DA, old information is updated through growth forecasts and when new information becomes available it is assimilated with the old information; the different sources of information are made use of to the extent motivated by their accuracy. In this study we made a general assessment of the usefulness of DA in connection with stand-level forest inventories and we compared two different methodological approaches, the extended Kalman filter and the Bayesian method. Not surprisingly, the relative advantage of DA was found to be largest for cases where low-precision estimates of growing stock volume were obtained at short intervals and forecasts were made with accurate growth prediction models. The methodological comparison revealed a tendency of the extended Kalman filter to underestimate the variance of the estimates.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".