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Record W2171906249 · doi:10.4039/n04-009

Biology and phenology of<i>Cecidophyopsis psilaspis</i>(Acari: Eriophyidae) on Pacific yew (Taxaceae)

2004· article· en· W2171906249 on OpenAlexaffabout
Valin G. Marshall, Marilyn Clayton

Bibliographic record

VenueThe Canadian Entomologist · 2004
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect-Plant Interactions and Control
Canadian institutionsCanadian Forest ServiceNatural Resources CanadaRoyal Roads University
Fundersnot available
KeywordsBiologyPhenologyEriophyidaeMiteBotanyAcariPopulationReproductive biologyEvergreenHorticultureDemography

Abstract

fetched live from OpenAlex

Abstract The biology and phenology of the yew big bud mite, Cecidophyopsis psilaspis (Nalepa, 1893), were studied on Pacific yew, Taxus brevifolia Nutt., in British Columbia, Canada. The mite showed the typical life cycle of eriophyoids on evergreen hosts, with all stases being present throughout the year. The numbers of C. psilaspis , which colonized both vegetative and reproductive buds, peaked in May to August, with the lowest numbers in March and October and the highest numbers in June. Mite numbers differed among bud types, with averages following the sequence terminal buds = lateral buds &gt; male reproductive buds &gt; axillary buds &gt; female reproductive buds &gt; latent buds. Very few mites were found in latent buds except during bud formation, when other vegetative buds were unavailable. Reproductive buds were colonized mostly from May to July. There was no evidence of arrhenotoky in C. psilaspis , as the proportion of mites that were females ranged from 54% to 100%. Temperature and predation were considered the likely factors that determine population fluctuations. It was hypothesized that C. psilaspis abundance increased following favorable spring temperatures and new food resources, whereas predation by other mite species and lower temperatures, which prolonged development, were responsible for the low numbers in March and October.

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 score0.518
Threshold uncertainty score0.692

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 head0.014
GPT teacher head0.218
Teacher spread0.204 · 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
Published2004
Admission routes2
Has abstractyes

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