Correlations, distributions, and trends in forest inventory errors and their effects on forest planning
Bibliographic record
Abstract
Errors in forest planning data are known to have various undesired effects, which have been examined previously by simulating their impact on forest planning systems. In most cases, the simulation of forest inventory errors has been simplified by assuming the error distribution to be Gaussian, possibly with a constant bias, and neglecting possible correlations between the errors in various attributes. The first aim here was to examine the distributions, correlations, and trends in errors when using alternative forest inventory methods, and the second was to analyse how different error simulation methods affect the estimated economic losses caused by suboptimal harvest timing on account of errors. We found that the errors were not normally distributed, had notable trends, and showed significant correlations between the errors for the various attributes. The most important factor affecting the inoptimality losses was the powerful tendency to underestimate the growing stock properties of mature stands. The error simulation method clearly makes a difference when analysing the effects of errors, and it is therefore important to use a simulation method that generates realistic errors.
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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 | 0.021 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".