Investigations of fungal pathogens of Douglas-fir on various provenance plots
Bibliographic record
Abstract
The bachelor thesis evaluates the incidence of fungal pathogens due to various provenance of Pseudotsuga menziesii Mirbel, Franco. The research was conducted on the provenance area Jizbice - LS Vlašim. In this area, the presence of fungus of the genus Armillaria was evaluated. By using standard phytopathological methods, samples of needles and offshoots were taken, and the occurrence of the various needle blight species was investigated. In the field the preliminary determination and quantification of the occurrence of fungus pathogens infestation were implemented. Consequently, the microscope method was used for the fungal species spectrum completion and specification in the laboratory. The fungus Rhabdocline pseudotsugae was discovered. The fungus Rhizosphaera genus was discovered on some needles from the researched provenances. The occurrence of the Armillaria spp. was not confirmed. It was determined, that it highly depends on the provenance of the infested tree. The most resistant provenance was Nimkish (1025), located in the north part of the Vancouver island, British Columbia. The least resistant provenance, in terms of defoliation, was provenance Merritt (1028) from British Columbia. Correspondence analysis results showed that the provenance of 1010 and 1028 showed the highest number of individuals and can be expected to have better resistance to abiotic and biotic effects of harmful factors.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".