Spatially consistent imputations of forest data under a semivariogram model
Bibliographic record
Abstract
This study proposes a method to perform spatially consistent imputations of forest data to serve simulation studies where spatial autocorrelation is expected to have an effect (e.g., sampling simulations and forest scenario analysis). Starting with a nearest neighbour imputation, an optimization process brings the spatially comprehensive data to a desired state, controlled by a target semivariogram and a target histogram. The target values for both parameters may be approximated using empirical data and are combined in the objective function used by the optimization algorithm. Here, we demonstrate a case study using wall-to-wall airborne laser scanner data, satellite data, and field observations for an 852 ha forest area in southern Norway. Different combinations of data types and target parameters were tested, and the target values were reached in most cases. In some cases, with a more restrictive objective function, the semivariogram did not completely converge to its target values, yet still had a convergence of at least 93%, expressed by the difference reduction between initial and target values. The results recommend the proposed method as a practical means to generate spatially explicit forest data when a particular distribution and well-defined spatial dependence are required.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".