Factors affecting pine pitch canker modelled on Michaelis–Menten kineticsThis article is one of a collection of papers based on a presentation from the<i>Stem and Shoot Fungal Pathogens and Parasitic Plants: the Values of Biological Diversity</i>session of the XXII International Union of Forestry Research Organization World Congress meeting held in Brisbane, Queensland, Australia, in 2005.
Bibliographic record
Abstract
Fusarium circinatum Nirenberg and O’Donnell is an important pathogen of pine seedlings and cuttings in South Africa. The fungus causes plant death in nurseries, as well as during establishment of pine plantations. The aim of this study was to consider the effects of wound type, spore concentration, and environmental stress on infection incidence. Pine seedlings were inoculated using three wounding methods and five spore concentrations. Inoculated seedlings were incubated under optimal environmental conditions, suboptimal conditions, and suboptimal conditions combined with a fungicide treatment. Results showed that the mean percentage infection caused by increasing spore concentrations can be described by the Michaelis–Menten function. The gradient of the function, as well as the asymptotic maximum level of infection, was dependant on environmental stress and the physiological state of the host, as well as the wounding method. Spore concentration had the highest influence on infection incidence in physiologically stressed seedlings. Fungicide treatment did not influence the rate of infection incidence in comparison with the treatments conducted under optimal environmental conditions, but significantly lowered the asymptotic maximum level of infection incidence. Seedlings wounded on the stems had the highest infection incidence, when compared with other wounding methods.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".