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Record W6912938180 · doi:10.5683/sp3/pfgumj

The effects of plant diversity and origin on abundance and diversity of syrphid flies (Diptera: Syrphidae) in small urban gardens

2023· dataset· en· W6912938180 on OpenAlexaff

Bibliographic record

VenueBorealis · 2023
Typedataset
Languageen
Field
Topic
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPollinatorAbundance (ecology)Species richnessGeneralist and specialist speciesNative plantHabitatUrban ecologyDiversity (politics)

Abstract

fetched live from OpenAlex

Gardens have emerged as a key habitat resource for pollinators in cities, but more research is needed to determine the optimal garden characteristics for maximizing native pollinator diversity. Syrphid flies (Diptera: Syrphidae) are abundant generalist fly pollinators that have received less study than other pollinators in urban gardens. In this study, we investigated whether flowering plant diversity and the presence of native plants were related to syrphid abundance and diversity in urban street gardens. Over a 2-month period, we sampled 12 small public gardens in a residential urban area to explore correlations between plant and syrphid assemblages. Though syrphid abundance and richness varied among gardens, neither floral richness nor the presence of native flowers was correlated with syrphid abundance or diversity. Beyond plant diversity and origin, other characteristics may be more important to syrphid visitation at the garden scale. Better understanding the role of garden characteristics among the complex factors shaping urban syrphid assemblages will offer valuable insights for the improvement of pollinator conservation strategies

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.042
Threshold uncertainty score0.084

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.004

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 head0.019
GPT teacher head0.223
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreDataset

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

Citations0
Published2023
Admission routes1
Has abstractyes

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