Predicting the severity of <i>Cyclaneusma minus</i> on <i>Pinus radiata</i> under current climate in New Zealand
Bibliographic record
Abstract
Despite being a damaging foliar disease of Pinus species, little research has characterized spatial variation in disease severity of Cyclaneusma needle cast at a macroscale. Using an extensive data set describing Cyclaneusma needle cast (Ssev) on plantation-grown Pinus radiata D. Don stands distributed widely across New Zealand, the objectives of this research were to (i) develop a regression model describing Ssev, (ii) use this model to identify key drivers of Ssev and their functional form and relative importance, and (iii) develop spatial predictions of Ssev for New Zealand P. radiata under current climate. Using an independent validation data set, the final model accounted for 73% of the variance in Ssev using four significant (P < 0.001) explanatory variables and an isotrophic exponential model to account for the spatial covariance in the data. Ssev was most sensitive to elevation followed by mean winter air temperature, mean relative humidity during July, and then stand age. Ssev increased to a maximum at mean winter air temperatures of between 7 and 9 °C before declining. Relationships between Ssev and all other variables were linear and positive. Spatial predictions of Ssev varied widely throughout New Zealand. Values of Ssev were highest in moderately warm, wet, and humid high-elevation environments located in the central North Island. In contrast, relatively low values of Ssev were predicted in drier eastern and southern regions of New Zealand.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".