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Record W2045769591 · doi:10.1080/07060661.2011.610826

Effect of iron and nitrogen on the development of<i>Helminthosporium solani</i>and potato silver scurf

2011· article· en· W2045769591 on OpenAlexafffundvenue
Benjamin Mimee, Tyler J. Avis, Sophia Boivin, Suha Jabaji, Russell J. Tweddell

Bibliographic record

VenueCanadian Journal of Plant Pathology · 2011
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Micronutrient Interactions and Effects
Canadian institutionsMcGill UniversityCarleton UniversityUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRhizoctonia solaniNitrogenBiologyAgronomyChemistryHorticultureOrganic chemistry

Abstract

fetched live from OpenAlex

Silver scurf is a surface blemish disease of potato (Solanum tuberosum L.) tubers caused by Helminthosporium solani Durieu & Mont.Silver scurf is becoming a disease of high economic impact.In this study, the effect of different iron (FeSO 4 , FeCl 2 ) and nitrogen (NaNO 2 , NaNO 3 , NH 4 Cl, NH 4 NO 3 ) salts on H. solani conidial germination and on potato silver scurf development was evaluated.The results show that iron and nitrogen salts affect in vitro germination of H. solani conidia.Conidia were particularly sensitive to FeSO 4 and FeCl 2 .These salts completely inhibited conidial germination and were shown to be toxic at a concentration of 0.9 mM.Among the nitrogen salts tested, conidia were most affected by NaNO 2 and NH 4 Cl, which almost completely inhibited their germination at a concentration of 169.7 mM.NaNO 2 was also toxic to conidia.Among the salts tested, only FeSO 4 , FeCl 2 and NaNO 2 reduced the development of silver scurf.Comparison of the effect of the different tested salts leads to the conclusion that ions Fe ++ and NO 2 -have toxic effects on the conidia and repressive effects on silver scurf development while NO 3 -and NH 4 + have no toxic effect on conidia and no repressive effect on silver scurf development.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.274
Threshold uncertainty score0.281

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 head0.015
GPT teacher head0.185
Teacher spread0.170 · 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

Citations4
Published2011
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Journal of Plant PathologySame topicPlant Micronutrient Interactions and EffectsFrench-language works237,207