From comprehensive field inventories to remotely sensed wall-to-wall stand attribute data — a brief history of management inventories in the Nordic countries
Bibliographic record
Abstract
Forest management inventories (FMIs) provide critical information, usually at the stand level, for forest management planning. A typical FMI includes (i) the delineation of the inventory area to stands by applying auxiliary information; (ii) the classification of the stands according to categorical attributes such as age, site fertility, main tree species, and stand development; and (iii) measurement, modelling, and prediction of stand attributes of interest. The emergence of wall-to-wall remote-sensing data has enabled a paradigm change in FMIs from highly subjective, visual assessments to objective, model-based inferences. Previously, optical remote-sensing data were used to complement visual assessments, especially in stand delineation and height measurements. The evolution of airborne laser scanning (ALS) has made objective estimation of forest characteristics with known accuracy possible. New optical and Lidar-based sensors and platforms will allow further improvements of accuracy. However, there are still bottlenecks related to species-specific stand attribute information in mixed stands and assessments of tree quality. Here, we concentrate on approaches and methods that have been applied in the Nordic countries in particular.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.020 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".