Assessing model performance in forecasting long-term individual tree diameter versus basal area increment for the primary Acadian tree species
Bibliographic record
Abstract
Tree basal area (ba) or diameter at breast height (dbh) are universally used to represent tree secondary growth in individual tree based growth models. However, the long-term implications of using either ba or dbh for predictions are rarely fully assessed. In this analysis, Δba and Δdbh increment equations were fit to identical datasets gathered from six conifer and four hardwood species grown in central Maine. The performance of Δba and Δdbh predictions from nonlinear mixed-effects models were then compared with observed growth measurements of up to 29 years via a Monte Carlo simulation. Two evaluation statistics indicated substantial improvement in forecasting dbh using Δdbh rather than Δba. Root mean squared error (RMSE) and percentage mean absolute deviation (MAD%) were reduced by 14% and 15% on average, respectively, across all projection length intervals (5–29 years) when Δdbh was used over Δba. Differences were especially noted as projection lengths increased. RMSE and MAD% were reduced by 24% when Δdbh was employed over Δba at longer projection lengths (up to 29 years). Simulations found that simulating random effects rather than using local estimates for random effects performed as well or better at longer interval lengths. These results highlight the implications that selecting a growth model dependent variable can have and the importance of incorporating model uncertainty into the growth projections of individual tree based models.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".