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Artificial covering on trap nests improves the colonization of trap‐nesting wasps

2008· article· en· W2036878591 on OpenAlexafffundabout
Hisatomo Taki, Peter G. Kevan, Blandina Felipe Viana, Fabiana Oliveira da Silva, Matthias Buck

Bibliographic record

VenueJournal of Applied Entomology · 2008
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of CanadaMinistry of Natural ResourcesRotary Foundation
KeywordsSphecidaeBiologyTrap (plumbing)HymenopteraNest (protein structural motif)EcologyParasitismVespidaeAbundance (ecology)ZoologyHost (biology)Geography

Abstract

fetched live from OpenAlex

Abstract To evaluate the role that a trap‐nest cover might have on sampling methodologies, the abundance of each species of trap‐nesting Hymenoptera and the parasitism rate in a Canadian forest were compared between artificially covered and uncovered traps. Of trap tubes exposed at eight forest sites in six trap‐nest boxes, 531 trap tubes were occupied and 1216 individuals of 12 wasp species of four predatory families, Vespidae (Eumeninae), Crabronidae, Sphecidae and Pompilidae emerged over 2 years, and no bee species were found. Results indicated that artificial covering led to a significant increase in the number of nested tubes ofAncistrocerus adiabatus,Ancistrocerus antilope,Ancistrocerus campestrisandAuplopus mellipes, and significant effects of covering were not found for the other species. No significant difference in the overall parasitism rate between covered and uncovered traps was noted. These suggested that the covering technique could provide more opportunities for some wasp species to colonize trap nests.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.053
GPT teacher head0.223
Teacher spread0.169 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations20
Published2008
Admission routes3
Has abstractyes

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