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Record W3166141715

MANAGEMENT OF LESION AND DAGGER NEMATODES WITH ROTATION CROPS

2021· article· en· W3166141715 on OpenAlexaboutno aff
J. A. LaMondia

Bibliographic record

VenueNematropica · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicNematode management and characterization studies
Canadian institutionsnot available
Fundersnot available
KeywordsBiologyPratylenchus penetransAgronomyPopulationCover cropNematodeEcology
DOInot available

Abstract

fetched live from OpenAlex

Annual rotation green manure crops of selected brassicas, buckwheat, forage pearl millet, forage radish, black-eyed Susan, sesame, sudangrass, and velvetbean were evaluated to determine impacts on lesion nematode Pratylenchus penetrans and dagger nematode Xiphinema americanum populations densities.  Canadian forage pearl millet ‘101’and ‘Tifgrain 102’ millet effectively controlled P.  penetrans but increased population densities of X. americanum.  Black-eyed Susan (Rudbeckia hirta) and sudangrass ‘Trudan 8’ also reduced P.  penetrans densites but not X. americanum.  Rapeseed and other brassicas as a green manure reduced X. americanum densities but did not suppress P.  penetrans.  Brassica juncea ‘Pacific Gold’, B. napus ‘Dwarf Essex’, but not mustard ‘Caliente’ resulted in low densities of X. americanum in soil.  Velvetbean and sesame increased both P.  penetrans and X. americanum population densities.  A moderately suitable host plant such as buckwheat was ineffective in managing both P.  penetrans and X. americanum populations.  These results emphasize that suppressive effects of any given cover crop are nematode-specific, and that within a group of cover crops, e.g., the Brassicaceae, species and varieties can vary substantially in their effectiveness.  If the rotation crop chosen is a good host for the nematodes present, it may exacerbate the problem instead of controlling it.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.746
Threshold uncertainty score0.143

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 head0.017
GPT teacher head0.218
Teacher spread0.201 · 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 designObservational
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
Published2021
Admission routes1
Has abstractyes

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