Evaluating behaviors of factors affecting the site index estimate on the basis of a single stand using simulation approach
Bibliographic record
Abstract
Height observations H1and H2present on the right- and left-hand sides of site index models, respectively. The error terms associated with H1and H2, along with parameter estimate errors, affect the estimate of the site index. Projection error variance (PEV), in a projection from A1to A2, consisted of four components associated with H1, H2, the covariance of H1and H2, and the parameter estimate errors. In this study, behaviors of these components were investigated via simulations on the basis of six equations derived from the Lundqvist–Kerf and the Hossfeld IV functions. Simulation results showed that projection interval, projection direction, and selected site-dependent parameter influenced PEV and its components. PEVs of backward and forward projections with the same projection interval lengths were remarkably different if the underlying model was anamorphic. With increasing projection interval length, the PEV of forward projections monotonically increased to a certain value, whereas the PEV of backward projections decreased to zero after reaching a maximum.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".