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Record W7095353955

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2014· article· en· W7095353955 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Pathogens and Resistance
Canadian institutionsnot available
Fundersnot available
KeywordsPhytophthora ramorumTwigHost (biology)BlightOrnamental plantPathogenPhytophthora
DOInot available

Abstract

fetched live from OpenAlex

The exotic, federally quarantined plant pathogen Phytophthora ramorum causes Sudden Oak Death (fig. 40.1) and ramorum shoot or leaf blight on more than 100 hosts from 36 different families. Hosts include a number of conifers, maples, tanoak, beech, and oak species (table 40.1). Although some hosts, such as oak and tanoak, develop stem cankers, most hosts only develop leaf spots and twig dieback when infected by P. ramorum, and are not usually killed by the pathogen. These diseases are commonly referred to as ramorum blight or dieback. In addition to hosts that have been naturally infected in the field, laboratory research indicates that a number of conifers, particularly many true firs and larch, may also be potential pathogen hosts. Distribution Naturally infected hosts have been reported in forests and landscapes in California and southwestern Oregon coastal areas. Infected ornamental nursery stock, has been detected throughout the United States and British Columbia, Canada. Despite efforts to eradicate the pathogen from nurseries, the pathogen has spread to waterways and plants outside of infected nurseries at a limited number of sites. The distribution of this disease is expected to increase over time. Damage Depending on the host species affected, P. ramorum may cause leaf spots, shoot blight, leaf and twig dieback, and outright tree mortality from stem cankers. Besides the direct damage this pathogen may cause, Federal and State regulatory actions associated with the detection of infected host Figure 40.1—Tanoaks killed by Phytophthora ramorum in a California forest. Photo by Gary Chastagner, Washington

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.903
Threshold uncertainty score0.376

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.009
GPT teacher head0.169
Teacher spread0.161 · 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 designBench or experimental
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

Citations0
Published2014
Admission routes1
Has abstractyes

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