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Record W2153325382 · doi:10.1603/0022-0493-94.4.831

Spatial and Temporal Patterns of Dispersal of Western Flower Thrips (Thysanoptera: Thripidae) in Nectarine Orchards in British Columbia

2001· article· en· W2153325382 on OpenAlexafffundabout
Isobel Pearsall, Judith H. Myers

Bibliographic record

VenueJournal of Economic Entomology · 2001
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsVancouver Island University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsThripsThripidaeWestern flower thripsOrchardBiologyCanopyGround levelBiological dispersalHorticultureBotanyGround floor

Abstract

fetched live from OpenAlex

Thrips were sampled from six nectarine orchards in the Dry Central Interior, British Columbia, Canada, between April and June 1993 using yellow sticky cards on posts spaced around the perimeter of each orchard. Although 12 identified species of thrips were captured, >90% of individuals were the western flower thrips, Frankliniella occidentalis (Pergande). The flight patterns and abundances of western flower thrips were compared between orchards located in two differently oriented valleys (N-S and E-W) and between orchards located close to or far from areas of wild land. Results indicate that densities of western flower thrips entering orchards, and their direction of movement, were related more to the external vegetation than either location within the two different valleys or general wind flow patterns. Western flower thrips tended to move into orchards close to ground level in early spring (late April and early May) but flew higher as ground cover grew taller and temperatures increased. Densities of western flower thrips at ground level were highest in an orchard with the densest dandelion ground cover. We conclude that the location of nectarine orchards in relation to wild areas is a major determinate of western flower thrips densities.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.836
Threshold uncertainty score0.926

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 head0.021
GPT teacher head0.217
Teacher spread0.196 · 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

Citations44
Published2001
Admission routes3
Has abstractyes

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