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Record W2119785921 · doi:10.7202/705983ar

Effects of nematicides on nematode population densities and crop yield in a turnip-corn-pea cropping system

2005· article· en· W2119785921 on OpenAlexaffvenue
A. W. Johnson, Clyde C. Dowler, N. C. Glaze, Donald R. Summer, Richard B. Chalfant, A. M. Golden, James E. Epperson

Bibliographic record

VenuePhytoprotection · 2005
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicNematode management and characterization studies
Canadian institutionsUniversité de MontréalQuebec Society for the Protection of Plants
FundersAgricultural Research ServiceUniversity of GeorgiaAndrew W. Mellon FoundationRichard King Mellon FoundationU.S. Department of Agriculture
KeywordsBiologyAgronomyTiftonMeloidogyne incognitaPopulationVignaCropping systemCropAldicarbNematodePesticideForage

Abstract

fetched live from OpenAlex

Four management systems were evaluated in a six-year study to control nematodes in a turnip-corn-pea annual cropping System on plots of Tifton loamy sand naturally infested with Meloidogyne spp . (about 90 % M. incognita and 10% M. hapla ), Pratylenchus spp. (about 65% P. scribneri, 25% P. brachyurus , and 10% P. zeae ), Paratrichodorus minor and Criconemella ornata . Turnip ( Brassica campestris subsp. rapifera ) supported low numbers of all nematodes. 'Pioneer 3369A' corn ( Zea mays ) supported greater numbers of all nematodes than 'Funks G-4507'. Population densities of Meloidogyne spp. juveniles (J 2 ) were suppressed below 80 per 150 cm 3 of soil by 98 % methyl bromide + 2 % chloropicrin and 20 % methyl isothiocyanate + 80 % chlorinated C 3 hydrocarbons on turnip and corn, and increased rapidly on 'Pinkeye purplehull', but not on a resistant cultivar, 'Worthmore' pea ( Vigna unguiculata ). Population densities of other nematodes were not affected by cultivar of pea or the nematicide, ethoprop. Fenamiphos was more effective than ethoprop in suppressing nematode population densities. Increases in crop yield in the intensive management system ranged from 4% to 52% over untreated controls.

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 score0.927
Threshold uncertainty score0.175

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 head0.012
GPT teacher head0.198
Teacher spread0.186 · 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

Citations1
Published2005
Admission routes2
Has abstractyes

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