Selecting models for capturing tree-size effects on growthresource relationships
Bibliographic record
Abstract
Subject trees included in growth analyses often vary in their initial size, possibly obscuring the effects of growth factors. We compare methods for incorporating size effects into growth models. For four different tree species, red maple (Acer rubrum L.), sugar maple (Acer saccharum Marsh.), American beech (Fagus grandifolia Ehrh.), and red oak (Quercus rubra L.), we compared models of radial growth rate of saplings as a function of light, water, and nitrogen availability that (i) ignored size effects on absolute growthresource relationships, (ii) related absolute growth rate (AGR) to size and resource availability, (iii) related relative growth rate (RGR) to resource availability, and (iv) related RGR to tree size and resource availability. Size effects explained 13%14% of variation in growth rates, and failure to account for these effects resulted in a substantial size bias in growth prediction. Overall, AGR-based models that included size as a predictor variable provided the best predictions and clearest interpretation of growthresource relationships across all growth model types and species examined. Modeling RGR without including size effects resulted in residual size bias. Including size as a predictor of RGR yielded nearly equivalent results to using size to predict AGR. We suggest that investigators evaluate both AGR- and RGR-based approaches and determine which is most appropriate for their study.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".