Evaluating sample plot imputation techniques as input in forest management planning
Bibliographic record
Abstract
Recent advances in the use of data from airborne laser scanners have produced results that are potentially useful for forest-management planning. In this study, the results from recently developed imputation techniques using laser scanner and satellite data were evaluated as input in a timber-oriented forestry planning context. Evaluation comprised a cost plus loss analysis in which the data cost for a specific method is added to the expected loss arising from nonoptimal forestry activities caused by erroneous forest descriptions. Forest data from sample plot imputations based on laser scanner data, satellite data, or a combination of both were available for 64 stands in southern Sweden. For comparison, sample plot field inventories of 5 and 10 plots were simulated for each stand. Different stand areas and real interest rates were tested. The best performing imputation method, using both laser scanner and satellite data, produced the lowest total cost plus loss in the smallest stands when using the highest interest rate. In all other cases, the sample plot methods performed better. Operative phase considerations, altering the original plan, would likely mitigate the effect of nonoptimal forest-management decisions, improving the competitiveness of the imputation methods. Further analysis should include such owner-specific considerations.
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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 | 0.005 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".