MétaCan
Menu
Back to cohort
Record W2891489136 · doi:10.5539/jas.v10n10p178

Severity of Septoria Leaf Spot and Sunflower Yield Due to Leaf Wetness Duration

2018· article· en· W2891489136 on OpenAlexvenueno aff
Silvane Isabel Brand, Arno Bernardo Heldwein, Sidinei Zwick Radons, Jocélia Rosa da Silva, Andressa Janaína Puhl

Bibliographic record

VenueJournal of Agricultural Science · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGrowth and nutrition in plants
Canadian institutionsnot available
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsLeaf wetnessSeptoriaSunflowerLeaf spotSowingHorticultureRelative humidityAgronomyYield (engineering)BiologyHelianthus annuusGeography

Abstract

fetched live from OpenAlex

The objective of this study was to evaluate the effect of leaf wetness on the severity of septoria leaf spot in sunflower. The experiments were performed in two sowing dates in November and January in Santa Maria, RS. Sunflower inoculation was carried out with the Septoria helianthi isolate, with subsequent assessment of disease severity, progress and cypsela yield. The treatments were composed of different periods of 0, 8, 12, 16, 20, 24, 28 and 32 hours of artificially applied leaf wetness. Variables influencing the disease were observed during the cycle, such as mean air temperature, mean relative air humidity and daily-accumulated rainfall. The greater the leaf wetness the greater the severity and the area under the disease progress curve. Meantime, the healthy leaf area duration has an inverse relation with leaf wetness duration. The yield also decreases with greater leaf wetness duration and the yield reduction reaches 100% in the plants exposed to the longer leaf wetness periods.

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)0.001
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 score0.894
Threshold uncertainty score0.220

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.016
GPT teacher head0.227
Teacher spread0.211 · 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

Citations5
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Agricultural ScienceSame topicGrowth and nutrition in plantsFrench-language works237,207