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Record W4313270746 · doi:10.5281/zenodo.7491865

Ekspresowa Analiza Zagrożenia Agrofagiem: Seiridium cardinale (W.W. Wagener) B. Sutton & I.A.S. Gibson

2021· report· en· W4313270746 on OpenAlexaboutno aff
Katarzyna Sadowska, Katarzyna Pieczul, Weronika Zenelt, Magdalena Gawlak, Daria Rzepecka, Agata Pruciak, Tomasz Kałuski

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2021
Typereport
Languageen
FieldSocial Sciences
TopicAgricultural economics and policies
Canadian institutionsnot available
Fundersnot available
KeywordsPhysicsArt

Abstract

fetched live from OpenAlex

<em>Seiridium cardinale</em> infests species in the <em>Cupressaceae</em> family, causing canker of cypresses. The main hosts of the agrophage are <em>Cupressus lawsoniana, C. macrocarpa, C. sempervirens</em> and <em>Thuja occidentalis</em>. The disease is manifested by yellowing or reddening of the leaves, which with time dry up and fall to the ground. The branches and tops of the trees die back. If there is a spread of infection in several places on one tree, it can lead to the death of the plant in a relatively short period of time depending on its age, susceptibility and environment. On the branches, at the point of penetration of the pathogen, a slight depression, a longitudinal crack and lenticular or elongated cankers with resinous exudate appear, sometimes there is necrosis of the bark. The pathogen appeared in 1927 in California, then spread to Canada, New Zealand, Asia Minor, South Africa and Europe. <em>S. cardinale</em> caused huge losses in the middle of the last century in France and Italy, and its presence was also confirmed in Germany. Due to the widespread occurrence of several host plants in the PRA area and changing climatic conditions, there is a risk of the agrophyte appearing in Poland.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.321
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0050.000
Scholarly communication0.0020.000
Open science0.0020.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0270.006

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.

Opus teacher head0.105
GPT teacher head0.319
Teacher spread0.214 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueZenodo (CERN European Organization for Nuclear Research)Same topicAgricultural economics and policiesFrench-language works237,207