Modelling the influence of weed competition on growth of young <i>Pinus radiata</i>. Development and parameterization of a hybrid model across an environmental gradient
Bibliographic record
Abstract
A hybrid tree growth model sensitive to competition from a diverse range of weed species was developed and validated using data from a nationwide series of 24 plots covering an extensive environmental gradient. Tree growth in plots with weeds over the first 3 years following establishment was predicted by reducing potential growth from an empirically determined optimum rate for the site (weed free) using a competition modifier, which accounts for the degree of weed competition for both light and water availability. Diameter growth of trees in plots with weeds was initially predicted by including a light competition modifier into the model developed for weed-free plots. This model accounted for 87% of the variance in diameter of trees growing with weeds. Although inclusion of this modifier provided unbiased predictions of tree diameter growth on wet sites, model predictions overestimated diameter growth on dryland sites by on average 11%. Addition of a competition index for water based on treatment differences in average fractional available root-zone volumetric water content significantly (p < 0.001) improved the precision (R2 = 0.96) and reduced the bias of the overall model. A cross validation of this final model indicated it was unbiased and relatively accurate (R2 = 0.95).
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".