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Record W2078792961 · doi:10.1080/07060660509507237

Conidial morphology and ecological characteristics as diagnostic tools for identifying<i>Claviceps purpurea</i>from salt-marsh habitats

2005· article· en· W2078792961 on OpenAlexvenueno aff
Alison J. Fisher, Joseph M. DiTomaso, Thomas R. Gordon

Bibliographic record

VenueCanadian Journal of Plant Pathology · 2005
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and fungal interactions
Canadian institutionsnot available
FundersUniversity of California, Davis
KeywordsConidiumBiologySalt marshHerbariumHabitatClaviceps purpureaMarshBotanyHalophyteEcologyWetlandSalinity

Abstract

fetched live from OpenAlex

Claviceps purpurea associated with grass hosts in salt-marsh habitats, also known as G3 ergot, can be differentiated from C. purpurea infecting grasses in other habitats, using genetic, chemical, and morphological criteria. However, only morphological analysis can be preformed on herbarium specimens, which should not be destroyed, or older samples that cannot be grown in culture. To determine if conidial characteristics could be employed to identify the three infraspecific groups of C. purpurea (G1–G3), sclerotia from terrestrial grasses (G1), moist habitats (G2), and salt-marsh habitats (G3) were examined. All G1 sclerotia sank in water. All G3 sclerotia floated in water, except those from Washington State, which had variable buoyancy characteristics. Group 2 sclerotia did not float in water, except those collected from Calamagrostis nutkaensis and Ammophila breviligulata. Based on the length of conidia derived from a plant host, G1 samples are indistinguishable from G2 samples but are significantly different from G3. Conidia produced in culture were on average smaller than conidia produced on plant hosts. These results indicate that ecological and conidial characteristics can be used to distinguish G3 from G1, but cannot consistently separate either from G2.

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)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.721
Threshold uncertainty score0.942

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.038
GPT teacher head0.248
Teacher spread0.210 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations2
Published2005
Admission routes1
Has abstractyes

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