Evaluating Local Calibration Methods for Improving Diameter Growth Predictions in the Southern Variant, Forest Vegetation Simulator (FVS-Sn)
Bibliographic record
Abstract
Abstract Local calibration methods were evaluated for diameter at breast height (dbh) growth predictions in 11 tree species in Virginia, USA, using a model form based on the Forest Vegetation Simulator Southern Variant (FVS-Sn) large tree dbh regression model. Data from 1090 remeasured forest inventory plots from the USDA Forest Service’s Forest Inventory and Analysis (FIA) database were used to calibrate FVS dbh growth predictions to local conditions and evaluate four calibration methods based on the following information: 1) median prediction errors calculated from locally observed dbh pairs before and after a five-year remeasurement period; 2) a random intercept estimated from locally observed dbh using mixed-effects regression; 3) a simple linear regression (SLR) model fitted to observed and predicted dbh at the local scale; and 4) an SLR model with regression through the origin. Calibration methods were assessed using leave-one-out cross-validation, comparing model predictions to observed dbh growth from withheld trees. Equivalence testing indicated median or regression-based local calibration methods achieved prediction-error tolerances over 5–7 year growth intervals as small as 0.11 cm (0.03 cm for two regression-based methods) for all species, whereas the random-intercept approach only achieved a minimum tolerance of 0.2 cm. Compared to uncalibrated models, local calibration substantially reduced prediction errors, demonstrating efficacy in increasing prediction accuracy, even with sparse FIA dbh growth data used for local-calibration.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".