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Record W6950563428 · doi:10.5683/sp3/96zlgc

Butterfly nectar foraging and flowering plant community data from field surveys in Victoria, British Columbia, Canada

2023· dataset· en· W6950563428 on OpenAlexaffabout

Bibliographic record

VenueBorealis · 2023
Typedataset
Languageen
Field
Topic
Canadian institutionsUniversity of British ColumbiaUniversity of Ottawa
Fundersnot available
KeywordsNectarForagingButterflyPhenologyNative plantPlant communityIntroduced species

Abstract

fetched live from OpenAlex

<b>Abstract</b><br/><p>The negative impacts of non-native species have been well documented, but some non-natives can play a positive role in native ecosystems. One way that non-native plants can positively interact with native butterflies is by provisioning nectar. Relatively little is known about the role of phenology in determining native butterfly visitation to non-native plants for nectar, yet flowering time directly controls nectar availability. Here, we investigate the phenological patterns of flowering by native and non-native plants and nectar foraging by native butterflies in an oak savanna on Vancouver Island, British Columbia, Canada. We also test whether native butterflies select nectar sources in proportion to their availability. We found that non-native plants were well integrated into butterfly nectar diets (83% of foraging observations) and that visitation to non-natives increased later in the season when native plants were no longer flowering. We also found that butterflies selected non-native flowers more often than expected based on their availability, suggesting that these plants represent a potentially valuable resource. Our study shows that non-native species have the potential to drive key species interactions in seasonal ecosystems. Management regimes focused on eradicating non-native species may need to re-consider their aims and evaluate resources that non-natives provide.</p>

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.158
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0030.003
Research integrity0.0000.002
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 head0.037
GPT teacher head0.256
Teacher spread0.219 · 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.

Study designNot applicable
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 routes2
Has abstractyes

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