Testing a juvenile tree growth model sensitive to competition from weeds, using <i>Pinus radiata</i> at two contrasting sites in New Zealand
Bibliographic record
Abstract
A juvenile tree growth model sensitive to competition from weeds was developed and tested. Tree growth is predicted by reducing potential growth from an empirically determined optimum rate for the site (weed-free) using a seasonally estimated competition modifier, which accounts for the degree of weed competition for both water and light availability. The model was tested against data from a field trial at a dryland site, where juvenile Pinus radiata D. Don trees were grown with and without competition from the woody weed broom (Cytisus scoparius (L.) Link). For trees in plots without broom, seasonal fluctuations in growth were adequately modelled by a single-term Fourier series, which showed that maximum rates of diameter growth occurred during early summer. Diameter growth of trees in plots with broom was initially predicted by including a light-competition modifier into the model developed for weed-free plots on sites not subject to growth-limiting seasonal water deficit. Although the light modifier reduced growth from the weed-free state by 12% over the first year and 25% over the second year, modelled values still significantly exceeded measured diameter growth. To account for this overprediction a competition modifier based on modelled root-zone water storage was added into the model. Predictions of diameter growth using this modified model corresponded closely to measured diameter growth in both treatments.
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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 | 0.001 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".