Accuracy of partially visually assessed stand characteristics: a case study of Finnish forest inventory by compartments
Bibliographic record
Abstract
In Finland, forest management planning is based on standwise assessment of forest variables. The data is traditionally gathered partly by (subjective) sampling and partly by visual assessment, which makes the accuracy assessments difficult. This study consists of an experiment where the visual assessments of field technicians were compared with the accurately measured values. The data consists of assessments from 18 sample plots made by 19 technicians. Each technician assessed four forest characteristics from each stand, for each tree species and each tree class. Basal area was observed in all cases; the other three variables varied according to 18 different measurement strategies. From these observations, mixed models were estimated to analyze to what extent the assessment errors depend on forest characteristics. Variation among both sample plots and field technicians was also considered. The results show that some of the variables could be interpreted as Berkson cases. The assessment errors were also often highly hetero sce dastic. Therefore, variance was explicitly modeled, and the final error models were estimated with weighted mixed regression using the variance estimates as weights. The results show clear variation among technicians, especially in characteristics that include personal judgment. The effect of training could be detected from variation between the technician groups. Furthermore, the broad-leaved tree classes were generally more difficult to assess than conifers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".