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Record W2121233761 · doi:10.7202/706130ar

Seasonal and vertical distribution of Meloidogyne hapla in organic soil

2005· article· en· W2121233761 on OpenAlexafffundvenueabout
G. Bélair

Bibliographic record

VenuePhytoprotection · 2005
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicNematode management and characterization studies
Canadian institutionsUniversité de Montréal
FundersAgriculture and Agri-Food Canada
KeywordsDaucus carotaBioassayBiologyPopulationMeloidogyne arenariaHorticulturePopulation densityAbundance (ecology)BotanyAgronomyNematodeEcology

Abstract

fetched live from OpenAlex

The seasonal population fluctuations of the northern root-knot nematode Meloidogyne hapla on car rot ( Daucus carota ), onion ( Allium cepa ), and weeds were observed on organic soils in southwestern Quebec. Lowest population densities of M. hapla juveniles (J 2 ) were recorded in July and August, followed by a peak in September and October in plots with carrot or weedy fallow. In onion, J 2 densities remained near or below the detectable level during most of the sampling period, but a small trend in population increase was also detected in the fall. The vertical distribution of M. hapla was similar in carrot weedy fallow, and onion plots. J 2 were regularly recovered from the four sampling depths (0-10,11-20, 21-30, and 31-40 cm). The numbers of J 2 were greater in the 0-20 cm depth than the 21-40 cm depth, with 67, 68, and 60% of the total M. hapla population in the 0-20 strata for carrot, weedy fallow, and onion, respectively. The tomato bioassay method was more sensitive than the Baermann pan method for detecting low M. hapla densities. Because of the poor correlation between J 2 densities in the soil and the number of galls on tomato roots in the bioassay, a measurement of J 2 abundance such as the Baermann pan method shoud be supported by bioassay to further assist growers in their decision process for the management of M. hapla in organic soil.

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 score0.935
Threshold uncertainty score0.124

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 head0.011
GPT teacher head0.196
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 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

Citations6
Published2005
Admission routes4
Has abstractyes

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