A validation and evaluation of the Prognosis individual-tree basal area increment model
Bibliographic record
Abstract
We subjected the individual-tree, aspatial basal area increment model developed for the Inland Empire Variant of the Forest Vegetation Simulator to validation and evaluation tests. We used a large set of independent data from the Forest Inventory and Analysis program that covers the geographic extent to which the model is usually applied. Equivalence tests did not validate the model as a predictive tool using nominated criteria, though they usually did validate the model structure as a theory. Design-unbiased estimates of prediction error suggest that the model overpredicts diameter and volume increment by 14% and 2%, respectively. Relationships between species, bias, and predictor variables suggest the model may overpredict most on productive sites. We spatially interpolated the model performance across the study area using thin-plate splines. The observed regional patterns are examined using a selection of cross-sectional transects, and reveal a complex relationship between bias and the way climate effects are incorporated in the model structure that involve differences between the fitting and testing data. The model structure is surprisingly robust, but the representation of climate effects should be a priority in future revisions.
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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 | 0.016 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".