MétaCan
Menu
Back to cohort
Record W2172045053 · doi:10.1094/pdis-91-11-1459

Relationship of Beet Curly Top Foliar Ratings to Sugar Beet Yield

2007· article· en· W2172045053 on OpenAlexaboutno aff
C.A. Strausbaugh, Anne M. Gillen, Stacey Camp, Clinton C. Shock, Eric P. Eldredge, John J. Gallian

Bibliographic record

VenuePlant Disease · 2007
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Disease Resistance and Genetics
Canadian institutionsnot available
FundersDow AgroSciencesU.S. Department of Agriculture
KeywordsSugar beetBiologyCultivarSugarInoculationYield (engineering)AgronomyHorticulturePlant disease resistanceFood science

Abstract

fetched live from OpenAlex

Sugar beet (Beta vulgaris) varieties were evaluated for disease resistance to curly top to establish if disease ratings made in inoculated nurseries correlated with disease ratings and yield in sugar beet crops exposed to natural disease outbreaks. Cultivars were planted both in inoculated curly top nurseries in Kimberly, ID, and in commercial cultivar trials in irrigated fields near Ontario, OR and Nampa, ID. Plants were evaluated for curly top using a rating scale of 0 (no symptoms) to 9 (dead). Moderate disease pressure in the Ontario (mean rating = 3.8) and Nampa (mean rating = 4.1) fields resulted in significant differences for disease rating, root yield, sugar content, and estimated recoverable sugar among cultivars. Disease ratings from both commercial fields were positively correlated (r = 0.91 and 0.82, P < 0.0001) with ratings from the inoculated nurseries. In commercial fields, root yield was negatively related to disease rating (r 2 = 0.47 and 0.39, P ≤ 0.0004). For each unit increase in disease rating (increasing susceptibility), root yield decreased 5.76 to 6.93 t/ha. Thus, curly top nurseries reliably predict curly top resistant cultivars for commercial cultivation.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.097
Threshold uncertainty score0.215

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 head0.027
GPT teacher head0.233
Teacher spread0.207 · 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

Citations17
Published2007
Admission routes1
Has abstractyes

Explore more

Same venuePlant DiseaseSame topicPlant Disease Resistance and GeneticsFrench-language works237,207