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Record W2906076346 · doi:10.1080/01647954.2018.1538257

Analysis of the phoretic relationships between mites of the genus <i>Trichouropoda</i> Berlese (Parasitiformes: Uropodina) and the longhorn beetle <i>Plagionotus detritus</i> (Linnaeus) (Coleoptera: Cerambycidae) based on multiannual observations in Białowieża Primeval Forest, Central Europe

2018· article· en· W2906076346 on OpenAlexfundno aff
Szymon Konwerski, Jerzy M. Gutowski, Jerzy Błoszyk

Bibliographic record

VenueInternational Journal of Acarology · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicStudy of Mite Species
Canadian institutionsnot available
FundersCanadian Forest Service
KeywordsLonghorn beetleBiologyAcariDetritusHost (biology)EcologyZoologyPopulationGenusBark beetleBark (sound)

Abstract

fetched live from OpenAlex

We studied the phoretic relationships between the mites of the genus Trichouropoda Berlese, 1916 (Uropodina) and the longhorn beetle Plagionotus detritus (Linnaeus, 1758) based on large samples of specimens (2050 beetles and 19,216 mites) collected in the Białowieża Primeval Forest in Central Europe. Phoresy was studied over the years 2014, 2015, and 2016, with the emphasis on multi-annual trends in host–phoront associations, seasonal changes in intensity of phoresy, proportion of carriers of mites in the beetle population, and preferences of phoretic deutonymphs for specific parts of the host’s body. The high repeatability in all the variables we studied indicates high stability of the phoretic relationships, probably resulting from the synchronization of life cycles of beetle dispersants and phoretic mites. The long-term coevolution of phoronts and carriers in a natural forest ecosystem, as well as the expected close association of mites with specific beetle-generated microhabitats in larval galleries, may be important factors affecting the strength of the phoretic relationship.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.011
Threshold uncertainty score0.323

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.026
GPT teacher head0.241
Teacher spread0.215 · 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
Published2018
Admission routes1
Has abstractyes

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