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Record W3080234516 · doi:10.3390/plants9091096

Lower Nitrogen Availability Enhances Resistance to Whiteflies in Tomato

2020· article· en· W3080234516 on OpenAlexafffund
S. Ramachandran, Sylvie Renault, John Markham, Jaime A. Verdugo, Marta V. Albornoz, Germán Ávila‐Sakar

Bibliographic record

VenuePlants · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect-Plant Interactions and Control
Canadian institutionsUniversity of ManitobaUniversity of WinnipegUniversity of Guelph
FundersUniversity of Winnipeg
KeywordsHerbivoreBiologyPEST analysisInfestationResistance (ecology)AgronomyShootCropPlant tolerance to herbivoryHorticultureBotany

Abstract

fetched live from OpenAlex

Soil nitrogen (N) supplementation via fertilizers may increase crop yields substantially. However, by increasing tissue N content, added N can make plants more attractive to herbivores, effectively reducing their resistance to herbivores (ability to avoid herbivore damage). In turn, greater pest infestation may cause more severe reductions in fruit production than a moderate N scarcity. In this study, we tested whether lower N supplementation results in greater resistance to whiteflies and lower fruit production in four tomato varieties. We assessed the effects of N availability on tolerance to herbivores (degree to which fitness is affected by damage) and tested for the long-hypothesized trade-off between resistance and tolerance. Plants grown at half of an agronomically recommended amount of N had greater resistance without a significant drop in fruit production. Tomato varieties differed in resistance and tolerance to whiteflies, and showed a clear trade-off between these modes of defense. Root:shoot ratios were greater at lower N, but had no clear relation to tolerance. We estimated that the economic benefit of decreasing N addition almost fully compensates for losses due to lower tomato production. Additionally, lower fertilization rates would contribute to reduce environmental costs of large-scale use of agrochemicals.

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 score0.764
Threshold uncertainty score0.895

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)0.0010.000

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 head0.016
GPT teacher head0.215
Teacher spread0.199 · 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

Citations7
Published2020
Admission routes2
Has abstractyes

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