Importance of seasonal growth patterns in modelling interactions between radiata pine and some common weed species
Bibliographic record
Abstract
Previously described weed competition models that use competition indices based on weed size relative to tree size (e.g., tree height divided by weed height) require models of weed growth and models of weed-free tree growth. A method is presented to model these using standard sigmoidal growth functions and to incorporate regular seasonal patterns of tree growth using Fourier series approximations. The method is tested against data from a field trial at Rotorua, New Zealand, in which Pinus radiata D. Don was grown both on its own and in competition with several common weed species. Weed and tree height were best modelled by a Weibull function, and tree ground line diameter by a Schumacher function. Seasonal fluctuations in both tree and weed growth were adequately modelled by a single-term Fourier series. All weed species showed very similar, strong seasonal fluctuations in height growth, peaking in February. Seasonal fluctuations in tree height growth, and especially diameter growth, were less marked and peaked earlier, in December. Simulations suggested that it is necessary to account for seasonal effects when modelling competition during the first year of growth, but that seasonal effects have less influence in subsequent years.
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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 | 0.002 |
| 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 | 0.000 | 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".