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<i>Retracted</i>: First report of <i>Clonostachys rosea</i> causing root rot of <i>Beta vulgaris</i> in North Dakota, USA

2020· article· en· W3112300384 on OpenAlexaboutno aff
ME Haque, Most Shanaj Parvin

Post-publication record

NatureRetraction
ReasonError in Text;Lack of Approval from Company/Institution;
Date6/17/2021 0:00
Flagged by OpenAlex?Yes

Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.

Bibliographic record

VenueNew Disease Reports · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant Pathogens and Fungal Diseases
Canadian institutionsnot available
Fundersnot available
KeywordsBiologyConidiumSugar beetRoot rotPotato dextrose agarHorticultureBotanyGenBankSporePathogenicityAgarMicrobiologyGene

Abstract

fetched live from OpenAlex

In August 2018, sugar beet plants with dull green and chlorotic foliage were observed in Hickson (46.6694°N, 96.8104°W), North Dakota. The taproots were found to have several circular brown to black necrotic lesions (Fig. 1) and the disease incidence was about 5%. Beet roots were washed to remove soil particles, surface-sterilised in a 10% NaOCl solution for 1 minute, and dipped twice in sterile water. Isolations were done on potato dextrose agar (72 hr at 25 ±2°C). All colonies were white and the surface was feathery (Fig. 2). Ten isolates were examined and these consistently had verticillate and penicillate conidiophores (primary and secondary) similar to those illustrated by Afshari & Hemmati (1). Conidia were 6.2 to 9.5 μm × 5.9 to 8.9 μm (Fig. 3). Five pure cultures were prepared by single spore isolation. The morphology of isolates was consistent with Clonostachys rosea (Moreira et al., 3; Sun et al., 5). DNA was extracted from four isolates using a Norgen Biotek Corp. protocol (Canada, Cat.27300). Isolates were confirmed via sequencing (GenScript, Piscataway, USA) using the internal transcribed spacer (ITS1F/ITS4). A BLAST search demonstrated that the 539 bp sequence was 100% identical to C. rosea (GenBank Accession No. KM519669.1). An annotated DNA sequence was deposited into GenBank as MN186772.1. Greenhouse pathogenicity tests were undertaken on sugar beet using the sequenced isolate. Three-week-old C. rosea cultures were mixed with vermiculite and perlite mixer (PRO-MIX FLX, USA) in plastic trays (61 ×38 × 25 cm). For the control treatment no inoculum was added. Sterile water (500 ml/ tray) was added to the mixer to maintain sufficient moisture. Ten seeds of sugar beet cv. Crystal 101 were sown per tray, and the trays replicated thrice with inoculated and control treatments and maintained at 22°C, 75% relative humidity. Plants were watered as needed to maintain adequate soil moisture conducive for plant growth and disease development. After eight weeks, plants were harvested and root rot assessed. Taproots of 16 of the 30 inoculated plants had similar root rot symptoms as described previously (Fig. 4). No disease was observed in control plants. Clonostachys rosea was consistently reisolated from the diseased taproots and its identity confirmed using morphological and molecular methods, thus fulfilling Koch's postulates. Clonostachys rosea has been reported commonly as a mycoparasite or saprotrophic species from soil and various plant materials (Schroers et al., 4). However, there are a few reports of C. rosea causing root rots in soybean in Minnesota (Bienapfl et al., 2) and in faba bean in Iran (Afshari & Hemmati, 2017). To our best knowledge, this is the first report of C. rosea causing root rot of sugar beet in the USA, or worldwide. The authors are thankful to Dr. R. Reeder for his suggestions and technical support.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.012
GPT teacher head0.224
Teacher spread0.212 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
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
Published2020
Admission routes1
Has abstractyes

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