Fungal diseases of conifers in the dendrological garden named after S.F. Kharitonov (Pereslavl-Zalessky)
Bibliographic record
Abstract
В данной работе приведено описание грибных болезней, оказывающих негативное влияние на фитосанитарное состояние хвойных пород, произрастающих в дендрологическом саду имени С.Ф. Харитонова города Переславль-Залесский Ярославской области. Обследованы посадки интродуцированных видов – сосны черной (Pinus nigra J.F. Arnold), сосны кедровой сибирской (Pinus sibirica Du Tour), сосны кедровой корейской (Pinus koraiensis Siebold & Zucc.), ели колючей (Picea pungens Engelm.), ели канадской (Picea glauca (Moench) Voss), гибрида ели колючей и ели канадской (Picea pungens f. glauca × Picea glauca), пихты сибирской (Abies sibirica Ledeb.). Наиболее распространенными и значимыми из идентифицированных болезней являются: побеговый рак (склеродерриоз) сосны черной, сосны кедровой сибирской и сосны кедровой корейской (возбудитель – Brunchorstia pinea (P. Karst.) Höhn.); красная пятнистость (дотистромоз) хвои сосны черной и сосны кедровой сибирской (возбудитель – Dothistroma septosporum (Dorog.) Morelet); побурение хвои (ризосфериоз) ели колючей (возбудитель – Rhizosphaera kalkoffii Bub.) и пихты сибирской (возбудитель – Rhizosphaera pini (Corda) Maubl.); почернение и отмирание почек (мегалосепториоз) ели колючей, ели канадской и гибрида ели колючей и ели канадской (возбудитель – Megaloseptoria mirabilis Naumov). This paper provides a description of fungal diseases that have a negative impact on the phytosanitary state of conifers growing in the dendrological garden named after S.F. Kharitonov city of Pereslavl-Zalessky, Yaroslavl region. Plantings of introduced species – black pine (Pinus nigra J.F. Arnold), Siberian stone pine (Pinus sibirica Du Tour), Korean stone pine (Pinus koraiensis Siebold & Zucc.), prickly spruce (Picea pungens Engelm.), Canadian spruce (Picea glauca) (Moench) Voss), a hybrid of prickly spruce and Canadian spruce (Picea pungens f. glauca × Picea glauca), Siberian fir (Abies sibirica Ledeb.). The most common and significant of the identified diseases are: shoot cancer (scleroderriosis) of black pine, Siberian stone pine and Korean stone pine (causative agent - Brunchorstia pinea (P. Karst.) Höhn.); red spotting (dotistromosis) of needles of black pine and Siberian cedar pine (causative agent - Dothistroma septosporum (Dorog.) Morelet); browning of needles (rhizospheriosis) of prickly spruce (pathogen - Rhizosphaera kalkoffii Bub.) and Siberian fir (pathogen - Rhizosphaera pini (Corda) Maubl.); blackening and dying off of the buds (megaloseptoria) of prickly spruce, Canadian spruce and a hybrid of prickly spruce and Canadian spruce (pathogen - Megaloseptoria mirabilis Naumov).
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".